RAG Triad Of metrics

The RAG Triad of Metrics in the context of Retrieval-Augmented Generation (RAG) refers to a set of three critical metrics used to evaluate the performance and effectiveness of RAG models. These metrics are:

1. Recall: Measures how well the retrieval component of the RAG model is performing. Recall assesses the ability of the model to retrieve relevant documents from the corpus. High recall means the retriever successfully finds most of the relevant information needed for generating accurate responses.
2. Precision: Evaluates the accuracy of the retrieved documents. Precision measures the proportion of relevant documents among the retrieved ones. High precision indicates that the retrieved documents are highly relevant and useful for generating accurate responses.
3. Generation Quality: Assesses the quality of the text generated by the RAG model. This metric evaluates how coherent, contextually appropriate, and informative the generated responses are. Generation quality can be measured using various sub-metrics such as BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), and human evaluations.

Detailed Explanation of Each Metric

1.Recall

• Definition: Recall is the fraction of relevant documents successfully retrieved by the model out of all the relevant documents available in the corpus.
• Importance: High recall ensures that the RAG model has access to a comprehensive set of relevant information, which is crucial for generating accurate and complete responses.
• Calculation: Recall=Number of relevant documents retrieved Total number of relevant documents in the corpus

2. Precision

• Definition: Precision is the fraction of retrieved documents that are relevant to the given query.
• Importance: High precision ensures that the retrieved documents are useful and relevant, reducing the noise and improving the quality of the generated responses.
• Calculation: Precision=Number of relevant documents retrieved Total number of documents retrieved

3. Generation Quality

• Definition: Generation quality measures the overall effectiveness of the text generated by the RAG model in terms of coherence, relevance, and informativeness.
• Importance: High generation quality means the model produces responses that are not only accurate but also well-structured and contextually appropriate.
• Sub-Metrics:
• BLEU: Measures the overlap between the generated text and a set of reference texts, commonly used for evaluating machine translation and text generation tasks.
• ROUGE: Assesses the quality of the summary by comparing the overlap of n-grams, word sequences, and word pairs between the generated text and reference texts.
• Human Evaluation: Involves human judges rating the quality of the generated responses based on various criteria such as relevance, fluency, and informativeness.

The Interplay Between the Metrics

The RAG Triad of Metrics represents a balanced approach to evaluating RAG models, emphasizing the importance of both retrieval and generation components. Here's how they interplay:

• Recall vs. Precision: There is often a trade-off between recall and precision. High recall might lead to retrieving a large number of documents, including some irrelevant ones, which can lower precision. Conversely, high precision might result in fewer documents being retrieved, potentially missing some relevant information, which can lower recall. Balancing these metrics is crucial for optimal performance.
• Retrieval vs. Generation Quality: Effective retrieval (high recall and precision) is essential for high-quality generation. If the retrieved documents are relevant and comprehensive, the generation component can produce more accurate and contextually appropriate responses. Poor retrieval quality can lead to subpar generation, regardless of the generation model's capabilities.

Importance of the RAG Triad of Metrics

The RAG Triad of Metrics is vital for evaluating and improving RAG models because it ensures a holistic assessment of both the retrieval and generation processes. By focusing on these metrics, developers can:

1. Identify Weaknesses: Pinpoint specific areas where the model may be underperforming, whether in retrieving relevant documents or generating high-quality text.
2. Guide Improvements: Use insights from the metrics to make targeted improvements in the retriever and generator components.
3. Ensure Balanced Performance: Maintain a balance between retrieval and generation quality to produce the most effective and reliable responses.

In summary, the RAG Triad of Metrics—Recall, Precision, and Generation Quality—provides a comprehensive framework for evaluating and optimizing Retrieval-Augmented Generation models, ensuring they deliver accurate, relevant, and high-quality responses.

The image describes the RAG Triad, a framework used in information retrieval and question-answering systems to ensure the quality and relevance of responses. Here's a breakdown of the components:

1. Query: This is the user's question or request for information.
2. Context: This refers to the information retrieved from a database or knowledge source in response to the query.
3. Response: This is the final answer or information provided to the user based on the query and context.

The RAG Triad focuses on three key aspects to ensure the quality of the response:

• Answer Relevance: This checks if the response is relevant to the user's query. It's about ensuring that the answer directly addresses what was asked.
• Context Relevance: This ensures that the context retrieved is relevant to the query. The context must contain information that is pertinent to answering the query correctly.
• Groundedness: This ensures that the response is supported by the context. The answer provided should be based on and justifiable by the information retrieved, ensuring accuracy and reliability.

The cyclic arrows indicate the iterative process of refining the query, context, and response to improve the overall quality and relevance of the information provided.

Answer relevance in Retrieval-Augmented Generation (RAG) systems is a key factor in evaluating the system's performance. Several metrics can be used to assess how relevant and accurate the generated answers are when compared to the expected or reference answers. Below are detailed explanations of the main metrics used to measure answer relevance in RAG systems:

1. Precision at K (P@K)

• Description: Precision at K measures the proportion of relevant documents among the top K retrieved documents.
• Explanation: This metric indicates how many of the top K documents retrieved by the system are actually relevant. Higher precision means the system is more accurate in retrieving relevant documents in the top K results.
• Example: If a query retrieves 10 documents and 7 of them are relevant, then Precision at 10 (P@10) is 0.7 or 70%.

2. Recall at K (R@K)

• Description: Recall at K measures the proportion of all relevant documents that are retrieved in the top K documents.
• Explanation: This metric shows how well the system retrieves all relevant documents within the top K results. Higher recall means the system is retrieving most of the relevant documents available.
• Example: If there are 20 relevant documents for a query and the system retrieves 15 of them in the top 50 results, then Recall at 50 (R@50) is 0.75 or 75%.

3. Mean Reciprocal Rank (MRR)

• Description: MRR measures the rank of the first relevant document in the list of retrieved documents.
• Explanation: This metric focuses on how quickly the first relevant document appears in the results. A higher MRR indicates that relevant documents are being found earlier in the retrieval process.
• Example: For three queries, if the first relevant documents are found at ranks 2, 1, and 4, the MRR is calculated as the average of the reciprocals: (1/2 + 1/1 + 1/4) / 3.

4. Normalized Discounted Cumulative Gain (NDCG)

• Description: NDCG evaluates the ranking quality by considering the position of relevant documents and discounting their relevance logarithmically.
• Explanation: This metric gives higher scores to relevant documents appearing earlier in the list, thus emphasizing the importance of ranking relevant documents higher. It is useful for understanding the quality of the ranking of the retrieved documents.
• Example: If relevant documents are at positions 1, 3, and 7, the NDCG score will be higher compared to if the same documents were at positions 4, 5, and 8.

5. Exact Match (EM)

• Description: EM measures the percentage of responses that exactly match the reference answers.
• Explanation: This is a strict metric often used in question-answering tasks, where an answer must be exactly correct to be considered relevant. Higher EM means the generated answers are precise and correct.
• Example: If the reference answer is "Paris" and the system generates "Paris," it gets an exact match. If it generates "The capital of France," it does not get an exact match.

6. BLEU (Bilingual Evaluation Understudy) Score

• Description: BLEU score measures the correspondence between the generated answer and one or more reference answers by evaluating n-gram overlap.
• Explanation: This metric evaluates how closely the generated text matches human-written reference texts by comparing overlapping sequences of words (n-grams). Higher BLEU scores indicate more similar text generation.
• Example: If the reference answer is "The Eiffel Tower is in Paris" and the generated answer is "Eiffel Tower is located in Paris," the BLEU score will reflect the similarity in terms of n-gram overlap.

Application in RAG Systems

In RAG systems, these metrics collectively help evaluate the system's performance by assessing both the retrieval and generation components. For instance:

• Precision at K, Recall at K, MRR, and NDCG are used to evaluate how well the retrieval component is fetching relevant documents.
• Exact Match and BLEU are used to evaluate the quality and accuracy of the generated answers.

By optimizing these metrics, RAG systems aim to provide responses that are not only relevant and accurate but also well-formed and similar to human-generated answers. This ensures that users receive high-quality, relevant information in response to their queries.

The image is a diagram of a concept in artificial intelligence (AI) called the Retrieval-Augmented Generation (RAG) triad. It’s a technique used to improve how large language models (LLMs) like me answer your questions.

Here’s a breakdown of the RAG triad:

• Query (shown in blue rectangle on left): This is the question you ask the LLM.
• Context (shown in blue rectangle on right): This is the information the LLM uses to answer your question. In RAG, context includes two things: The LLM’s internal knowledge (like a giant encyclopedia of information) Information retrieved from a database in response to your question (like searching the web)
• Response (not shown in the image): This is the answer the LLM generates after considering the query and context.

The RAG triad focuses on two aspects of a good response:

• Relevance to the query: Does the answer address your question?
• Relevance to the context: Is the information in the answer supported by what the LLM knows and what it found?

By considering both these aspects, RAG aims to improve the accuracy and completeness of the information I provide in response to your questions.

Analogy: RAG Triad like a Student Writing a Paper

Imagine a student writing a research paper. The student has some general knowledge on the topic (like my internal knowledge base), but to write a good paper, they also need to consult resources like textbooks and articles (like the retrieved information in RAG).

The RAG triad is like a three-step process that helps the student do this:

1. The student gets a question (Query).
2. The student looks for information from reliable sources that might be helpful to answer the question (Retrieval).
3. The student uses that information, along with what they already know, to answer the question in a comprehensive way (Response).

In the context of Retrieval-Augmented Generation (RAG) systems, a feedback function plays a crucial role in evaluating and improving the performance of the model. Let’s break down what a feedback function is, how it operates, and why it is important specifically for RAG systems by reviewing inputs, outputs, and intermediate results.

Understanding the Feedback Function in RAG Systems

1. What is a Feedback Function?

A feedback function in a RAG system is a mechanism that provides a score or evaluation based on an analysis of the model’s performance. This involves reviewing:

• Inputs: The queries or prompts given to the model.
• Outputs: The responses generated by the model.
• Intermediate Results: The internal processing steps, such as the retrieved documents or intermediate generated text.

The feedback function uses these elements to produce a score that reflects the quality and effectiveness of the model’s performance. This score helps developers and researchers understand where improvements are needed and how well the model meets its objectives.

2. How Does the Feedback Function Work in RAG?

Here’s a step-by-step explanation of the feedback function’s role in a RAG system:

a. Reviewing Inputs

• What It Does: Evaluates the quality of the queries or prompts sent to the model.
• Why It Matters: Properly structured and relevant inputs are essential for retrieving useful information and generating accurate responses.
• Example: If a query like “Tell me about Paris” is vague, the feedback function might suggest a more specific query like “What are the historical landmarks in Paris?” for better retrieval results.

b. Reviewing Intermediate Results

• What It Does: Analyzes the documents or information retrieved by the retrieval component of the RAG system.
• Why It Matters: Ensures that the documents or data retrieved are relevant to the input query and that the retrieval process is effective.
• Example: If the retrieval component fetches irrelevant documents, the feedback function might score the system poorly and suggest refining the retrieval algorithm or improving document selection criteria.

c. Reviewing Outputs

• What It Does: Evaluates the responses generated by the model based on the retrieved documents and the input query.
• Why It Matters: The final responses should be accurate, relevant, coherent, and useful for the user’s query.
• Example: If the output is “The Eiffel Tower is in Berlin,” the feedback function will identify this as incorrect and score it lower, indicating that the model needs better generation strategies or improved document understanding.

d. Scoring and Reporting

• What It Does: Assigns scores based on the evaluation of inputs, intermediate results, and outputs.
• Why It Matters: Provides actionable insights for improving the RAG system’s components.
• Example: Scores might be based on metrics like Precision, Recall, MRR, NDCG, BLEU, or Exact Match. These scores help developers know which aspects of the system to refine, such as improving retrieval accuracy or enhancing response generation.

3. Metrics Used in Feedback Functions

The feedback function uses various metrics to provide a comprehensive evaluation of the RAG system’s performance:

• ****Precision at K (P@K): Measures the proportion of relevant documents among the top K retrieved documents.
• ****Recall at K (R@K): Measures the proportion of all relevant documents that are retrieved within the top K results.
• ****Mean Reciprocal Rank (MRR): Measures the rank of the first relevant document.
• ****Normalized Discounted Cumulative Gain (NDCG): Evaluates the ranking quality of the retrieved documents.
• ****Exact Match (EM): Measures the percentage of responses that exactly match the expected answers.
• BLEU Score: Measures the overlap between generated text and reference text based on n-grams.

4. Importance of the Feedback Function in RAG Systems

The feedback function is crucial for several reasons:

• Improves Retrieval and Generation: It identifies weaknesses in both the retrieval and generation stages, helping to refine these components for better performance.
• Guides Iterative Development: Provides data-driven insights for developers to iteratively improve the RAG system through fine-tuning and adjustments.
• Ensures Quality and Relevance: Helps maintain high standards for the accuracy, relevance, and coherence of the model’s outputs.
• Supports User Satisfaction: By improving the quality of responses and the relevance of information, it enhances the overall user experience.

Summary

In a RAG system, the feedback function is a critical tool that evaluates the effectiveness of the model by providing scores based on the review of inputs, intermediate results, and outputs. This evaluation process involves several key metrics and helps developers improve the system’s performance through a data-driven approach. Here’s a simplified breakdown:

• Inputs: Evaluate the quality and relevance of queries.
• Intermediate Results: Assess the relevance of retrieved documents.
• Outputs: Check the accuracy, relevance, and coherence of generated responses.
• Scoring: Assign scores using metrics like Precision, Recall, MRR, NDCG, EM, and BLEU.
• Reporting: Provide insights for further development and improvement.

By effectively using the feedback function, RAG systems can be continuously refined to produce more accurate, relevant, and high-quality responses.

Diagram of Feedback Function in RAG Systems

Here’s a visual representation of how the feedback function operates in a RAG

+---------------------+
|      User Query     |
+---------------------+
           |
           V
+---------------------+
|   Retrieval Model  |
| (Fetches Documents)|
+---------------------+
           |
           V
+---------------------+
| Intermediate Results|
|   (Retrieved Docs)  |
+---------------------+
           |
           V
+---------------------+
|  Generation Model  |
| (Generates Response)|
+---------------------+
           |
           V
+---------------------+
|     Output Response |
+---------------------+
           |
           V
+---------------------+
|   Feedback Function |
| (Evaluates Inputs,   |
|  Intermediate Results,|
|  Outputs)            |
+---------------------+
           |
           V
+---------------------+
|     Scoring and     |
|    Reporting        |
+---------------------+
           |
           V
+---------------------+
|    Improvement     |
|    Suggestions     |
+---------------------+        

This diagram shows the flow from user query to feedback evaluation and improvement suggestions, highlighting the role of the feedback function in enhancing the RAG system’s performance.

The feedback function in the context of a Retrieval-Augmented Generation (RAG) system is a structured process designed to evaluate and improve the performance of the model by analyzing its inputs, intermediate results, and outputs. Here’s a detailed explanation of the structure of a feedback function, broken down into its key components and processes:

Structure of the Feedback Function

1. Input Evaluation

• Description: This initial step focuses on assessing the quality of the input queries or prompts provided to the RAG system.
• Components:
• Example: If the query is “Tell me about cars,” the feedback function might suggest specifying the type of car or the aspect of cars (e.g., “Tell me about electric cars”).

2. Intermediate Results Analysis

• Description: This step evaluates the documents or data retrieved by the retrieval component before the generation process.
• Components:
• Examples of Evaluation:
• Example: If the retrieval component fetches documents that are mostly unrelated to the query, the feedback function might score it low and suggest improving the retrieval strategy.

3. Output Evaluation

• Description: This step evaluates the final responses generated by the RAG system.
• Components:
• Examples of Evaluation:
• Example: If the response to “What are the benefits of electric cars?” is “Electric cars are fast,” the feedback function might note that the response lacks detail and does not fully address the question.

4. Scoring and Reporting

• Description: This step involves quantifying the results of the evaluations and generating reports.
• Components:
• Examples of Evaluation Metrics:
• Example: The feedback function might produce a report showing that Precision at 10 is 70%, MRR is 0.5, and the response quality score is 4 out of 5. This report guides developers on which areas need improvement.

5. Improvement Suggestions

• Description: Based on the feedback and scores, this step provides recommendations for enhancing the RAG system.
• Components:
• Examples of Suggestions:
• Example: If the intermediate results were irrelevant, a suggestion might be to enhance the retrieval component’s ability to identify high-quality documents.

Diagram of Feedback Function Structure

Here’s a visual representation of the structure of the feedback function:

+---------------------+
|      User Query     |
+---------------------+
           |
           V
+---------------------+
|   Retrieve Documents|
|   (Retrieval Model) |
+---------------------+
           |
           V
+---------------------+
| Intermediate Results|
|   (Retrieved Docs)  |
+---------------------+
           |
           V
+---------------------+
|   Generate Response |
|  (Generation Model) |
+---------------------+
           |
           V
+---------------------+
|   Feedback Function |
|  (Evaluates Inputs, |
|   Intermediate Results, |
|   Outputs)          |
+---------------------+
           |
           V
+---------------------+
|   Scoring and       |
|   Reporting         |
+---------------------+
           |
           V
+---------------------+
|   Improvement       |
|   Suggestions       |
+---------------------+        

Detailed Breakdown of Each Component

1. Input Evaluation:
2. Intermediate Results Analysis:
3. Output Evaluation:
4. Scoring and Reporting:
5. Improvement Suggestions:

Summary

The feedback function in a RAG system is a multi-step process designed to evaluate the system’s performance by reviewing inputs, intermediate results, and outputs. It involves:

• Input Evaluation: Assessing query quality.
• Intermediate Results Analysis: Reviewing document retrieval effectiveness.
• Output Evaluation: Checking the accuracy, relevance, and quality of responses.
• Scoring and Reporting: Quantifying performance and generating reports.
• Improvement Suggestions: Providing recommendations for enhancing the system.

This structured approach ensures that the RAG system can be continuously improved to better meet user needs and achieve higher performance standards.

Example Feedback Function Workflow

Here’s a detailed example workflow of how the feedback function might operate in a RAG system:

+--------------------+
|    User Query      |
|  "Tell me about    |
|  the Eiffel Tower" |
+--------------------+
          |
          V
+----------------------+
|  Input Evaluation    |
|  - Checks query      |
|  - Scores: 0 or 1    |
+----------------------+
          |
          V
+----------------------+
| Retrieve Documents   |
| - Gets relevant docs |
|  ("Doc 1", "Doc 2")  |
+----------------------+
          |
          V
+----------------------+
| Intermediate Results |
| Evaluation           |
| - Checks docs        |
| - Scores: 0 or 1     |
+----------------------+
          |
          V
+----------------------+
| Generate Response    |
| - Produces answer    |
|  "The Eiffel Tower   |
|   is in Paris"       |
+----------------------+
          |
          V
+----------------------+
| Output Evaluation    |
| - Checks response    |
| - Scores: 0 or 1     |
+----------------------+
          |
          V
+----------------------+
| Scoring and Reporting|
| - Combines scores    |
| - Gives feedback     |
|   score              |
+----------------------+
          |
          V
+----------------------+
| Improvement          |
| Suggestions          |
| - Based on feedback  |
|   score              |
+----------------------+        

Explanation of Each Step

1. Input Evaluation:

• Process: The input query is checked for quality (e.g., specificity and clarity).
• Scoring: If the query meets the criteria, it scores 1; otherwise, it scores 0.

2.Intermediate Results Analysis:

• Process: The documents retrieved by the retrieval model are evaluated for relevance and quality.
• Scoring: If all documents are relevant, it scores 1; otherwise, it scores 0.

3.Output Evaluation:

• Process: The generated response is evaluated for accuracy, relevance, and coherence.
• Scoring: If the response is accurate and relevant, it scores 1; otherwise, it scores 0.

4.Scoring and Reporting:

• Process: The individual scores from each evaluation step are combined to produce a final feedback score.
• Feedback Score: The average of input, intermediate, and output scores.

5.Improvement Suggestions:

• Process: Based on the feedback score, suggestions are provided to improve the RAG system.
• Suggestions: Tailored recommendations to enhance query specificity, document relevance, or response accuracy.

By understanding and implementing this structured approach, developers can effectively use the feedback function to enhance RAG systems and achieve better results for users.

1.Title Section:

• The title of the image is "Context Relevance," indicating the main focus of the content.

2.Main Illustration:

• Two Blue Boxes:

• The left box is labeled "Query."
• The right box is labeled "Context."
• Connecting Arrow:

• There is an arrow pointing from the "Query" box to the "Context" box, symbolizing the relationship between a user's query and the retrieved context.

3.Caption:

• Below the illustration, the caption reads: "Context Relevance: How good is the retrieval?" This explains the essence of the concept depicted in the image, emphasizing the importance of the quality of retrieval in relation to the given query.

Explanation:

• The image illustrates the concept of context relevance in information retrieval systems. It highlights the process where a query is made by a user, and the system retrieves context in response to that query. The key idea is to evaluate how well the retrieved context matches the original query, which is crucial for effective information retrieval.

The image compares two excerpts to determine their relevance to the question: "How can altruism be beneficial in building a career?" Each excerpt is presented in a speech bubble with a relevance score.

Left Bubble (Relevance: 0.5)

Main Points:

• Successful people develop good habits in various aspects of life such as eating, exercise, sleep, relationships, work, learning, and self-care.
• These habits help maintain health and forward progress.
• Personal Discipline: Those who help others often achieve better outcomes themselves.
• Altruism: Helping others as a way to build one's own career.
• Imposter Syndrome: Discusses newcomers to AI feeling like frauds despite success and encourages them to not be discouraged.

Context:

• The excerpt focuses on the benefits of personal habits and discipline.
• Emphasizes that helping others (altruism) can improve one's own journey and career.
• Touches on overcoming imposter syndrome in the AI community.

Relevance to Altruism:

• Moderately relevant as it connects personal growth and career success to helping others.
• Includes a broad range of topics not directly related to job searching or career strategies.

Right Bubble (Relevance: 0.7)

Main Points:

• Using Informational Interviews: Finding the right job through informational interviews.
• Job Searching Tips:

• Research roles and companies.
• Apply directly or get referrals.
• Increase chances of finding a supportive position.

• Career Growth:

• Informational interviews can help identify positions that foster career development.
• Practical steps to improve job search outcomes.

• Context:

• Focuses on practical advice for job searching and leveraging networking.
• Highlights how informational interviews and referrals can aid in career growth.

Relevance to Altruism:

• More directly relevant as it provides actionable advice for career advancement.
• While not explicitly about altruism, it implies that helping others (through informational interviews) can lead to mutual benefits.

Conclusion

• Left Bubble (0.5): Connects altruism with personal habits and overcoming challenges, offering a broad perspective on career growth.
• Right Bubble (0.7): Offers practical career advice, making it slightly more relevant to the question of how altruism (networking, informational interviews) can benefit career building.

The right bubble scores higher in relevance because it provides specific strategies that align more closely with the practical aspects of building a career through altruistic actions like networking and informational interviews.

This image evaluates the relevance of a text excerpt in addressing the question "How can altruism be beneficial in building a career?" The evaluation assigns a relevance score of 0.7 and provides supporting evidence for this assessment.

Text Excerpt: The text discusses the importance of finding the right job and provides specific strategies to increase the likelihood of success in the job search process. It suggests researching roles and companies online or by talking to friends, and optionally arranging informal informational interviews with people in companies that appeal to you. Additionally, it recommends obtaining referrals from someone on the inside if possible.

Supporting Evidence: The supporting evidence explains that the statement provides practical information on how to find the right job and increase the chances of securing a position that supports a thriving career. By suggesting research, networking with friends, and arranging informational interviews, the text emphasizes the value of building connections and seeking advice from others. These activities can be seen as forms of altruism, as they involve both giving and receiving help, which can lead to mutual benefits in career building. This approach can offer insights into job opportunities and enable individuals to make more informed decisions about their career paths.

Context Relevance Score: 0.7 The score of 0.7 indicates a high level of relevance, as the text directly addresses the question by highlighting how altruistic behaviors, such as networking and informational interviews, can be beneficial in building a career.

This image outlines a process for evaluating and iterating on a system using the RAG (Retrieval-Augmented-Generation) framework with LlamaIndex and TruLens. The process is broken down into several steps:

1. Start with LlamaIndex Basic RAG: Begin the evaluation process using the basic RAG metrics provided by LlamaIndex. This step establishes a baseline for comparison.
2. Evaluate with TruLens RAG Triad: Use the TruLens RAG Triad to assess the system. This step includes identifying failure modes that are related to the context size, helping to understand where the system may be falling short.
3. Iterate with LlamaIndex Sentence Window RAG: Adjust the evaluation process by using the LlamaIndex Sentence Window RAG. This involves breaking down the context into smaller, more manageable windows for a more detailed assessment.
4. Re-evaluate with TruLens RAG Triad: After iterating, re-evaluate the system with the TruLens RAG Triad. This step focuses on checking for improvements in context relevance and other metrics that are critical to the system’s performance.
5. Experiment with Different Window Sizes: Conduct experiments with varying window sizes to determine which size yields the best evaluation metrics. This helps optimize the context size for the most accurate and relevant assessments.

By following these steps, the process aims to refine the evaluation method, improve context relevance, and enhance the overall performance of the system through iterative adjustments and targeted experiments.

Certainly! Feedback functions in language models (LMs) can be implemented in various ways, and these methods can be categorized into scalable and meaningful approaches.

Scalable Feedback Functions

These methods focus on evaluating language models in a way that can be scaled to handle large amounts of data or many instances of feedback:

1.Traditional NLP Evaluations:

• Description: These are classic evaluation metrics used in Natural Language Processing (NLP) for various tasks such as classification, sentiment analysis, and named entity recognition. Common metrics include accuracy, precision, recall, F1 score, and BLEU score for translation.
• Scalability: These metrics are well-defined and can be applied across large datasets, making them scalable for evaluating different models and configurations.

2.Masked Language Model (MLM) Evaluations:

• Description: In MLM evaluations, the model's ability to predict masked words or tokens in a sentence is assessed. This approach tests how well the model understands and generates text based on context.
• Scalability: Since this approach relies on standard test datasets and is algorithmically driven, it can be applied to large-scale data efficiently.

3.Language Model Evaluations (LLM Evaluations):

• Description: These evaluations assess the performance of large language models (LLMs) based on their general ability to perform a variety of language tasks. This can include benchmarks like GPT-4’s performance on diverse NLP tasks.
• Scalability: LLM evaluations are typically conducted using broad test sets and automated metrics, which allows for extensive and scalable assessment across different LLM architectures.

Meaningful Feedback Functions

These methods emphasize the quality and relevance of feedback for improving the model’s performance:

1.Human Evaluations:

• Description: Human evaluators assess the model’s outputs based on qualitative aspects such as coherence, relevance, and creativity. This can involve tasks like manual review of generated text or comparison of outputs.
• Meaningfulness: Human evaluations are more nuanced and can capture aspects of model performance that automated metrics might miss, such as contextual appropriateness and user satisfaction.

2.Ground Truth Evaluations:

• Description: These evaluations compare model outputs against a set of predefined, accurate answers or benchmarks. Ground truth refers to a reference standard against which model performance can be measured.
• Meaningfulness: This method ensures that the feedback is based on correct and verified information, providing a clear measure of how well the model aligns with known correct answers or solutions.

Combining Approaches

Often, a combination of scalable and meaningful methods is used to get a comprehensive view of model performance. For instance, automated evaluations might handle large-scale testing, while human feedback provides deep insights into specific issues.+

Example Workflow

1. Automated Testing:

• Use Traditional NLP Evaluations and MLM Evaluations to process large amounts of data and gather initial performance metrics.

2.In-Depth Analysis:

• Employ Human Evaluations to review specific cases or scenarios where automated metrics might be insufficient.

3.Benchmarking:

Use Ground Truth Evaluations to validate the model's outputs against known correct answers.

By combining these approaches, developers can ensure both broad and detailed evaluations of their language models.

1.Honest: This category focuses on the accuracy and relevance of the AI's outputs.

• Answer relevance: Ensures responses directly address the given query.
• Embedding distance: Measures how close the AI's output is to expected responses in a vector space.
• BLEU, ROUGE: These are standard metrics for evaluating machine translation and summarization quality.
• Summarization quality: Assesses how well the AI condenses information.
• Context Relevance: Checks if responses are appropriate to the broader conversation context.
• Groundedness: Ensures outputs are based on factual information rather than hallucinations.

2.Harmless: This section aims to prevent the AI from producing harmful or inappropriate content.

• PII Detection: Identifies and protects personally identifiable information.
• Toxicity: Measures and prevents offensive or harmful language.
• Stereotyping: Checks for biased or discriminatory outputs.
• Jailbreaks: Tests resistance to prompts designed to bypass safety measures.

3.Helpful: This category evaluates the AI's ability to provide useful and coherent assistance.

• Sentiment: Assesses the emotional tone of responses.
• Language mismatch: Checks for consistency in language use.
• Conciseness: Evaluates the brevity and clarity of responses.
• Coherence: Ensures logical flow and consistency in longer outputs.

Conclusion

Evaluating RAG models based on these three criteria helps ensure that the responses generated are not only accurate and reliable but also safe and useful for users. Each of these aspects requires specific evaluation methods and practices to ensure the model performs well across different dimensions of interaction with users.

Code:-

 import warnings
warnings.filterwarnings('ignore')
        

import utils

import os
import openai
openai.api_key = utils.get_openai_api_key()
        

from trulens_eval import Tru

tru = Tru()
tru.reset_database()
        

from llama_index import SimpleDirectoryReader

documents = SimpleDirectoryReader(
    input_files=["./eBook-How-to-Build-a-Career-in-AI.pdf"]
).load_data()
        

from llama_index import Document

document = Document(text="\n\n".\
                    join([doc.text for doc in documents]))
        

from utils import build_sentence_window_index

from llama_index.llms import OpenAI

llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)

sentence_index = build_sentence_window_index(
    document,
    llm,
    embed_model="local:BAAI/bge-small-en-v1.5",
    save_dir="sentence_index"
)
        

from utils import get_sentence_window_query_engine

sentence_window_engine = \
get_sentence_window_query_engine(sentence_index)
        

output = sentence_window_engine.query(
    "How do you create your AI portfolio?")
output.response        

Feedback functions

 import nest_asyncio

nest_asyncio.apply()
        

from trulens_eval import OpenAI as fOpenAI

provider = fOpenAI()        

1. Answer Relevance

 from trulens_eval import Feedback

f_qa_relevance = Feedback(
    provider.relevance_with_cot_reasons,
    name="Answer Relevance"
).on_input_output()        

2. Context Relevance

 from trulens_eval import TruLlama

context_selection = TruLlama.select_source_nodes().node.text
        

import numpy as np

f_qs_relevance = (
    Feedback(provider.qs_relevance,
             name="Context Relevance")
    .on_input()
    .on(context_selection)
    .aggregate(np.mean)
)
        

import numpy as np

f_qs_relevance = (
    Feedback(provider.qs_relevance_with_cot_reasons,
             name="Context Relevance")
    .on_input()
    .on(context_selection)
    .aggregate(np.mean)
)        

3. Groundedness

 from trulens_eval.feedback import Groundedness

grounded = Groundedness(groundedness_provider=provider)
        

f_groundedness = (
    Feedback(grounded.groundedness_measure_with_cot_reasons,
             name="Groundedness"
            )
    .on(context_selection)
    .on_output()
    .aggregate(grounded.grounded_statements_aggregator)
)        

Evaluation of the RAG application

 from trulens_eval import TruLlama
from trulens_eval import FeedbackMode

tru_recorder = TruLlama(
    sentence_window_engine,
    app_id="App_1",
    feedbacks=[
        f_qa_relevance,
        f_qs_relevance,
        f_groundedness
    ]
)
        

eval_questions = []
with open('eval_questions.txt', 'r') as file:
    for line in file:
        # Remove newline character and convert to integer
        item = line.strip()
        eval_questions.append(item)
        

eval_questions
        

eval_questions.append("How can I be successful in AI?")
        

eval_questions
        

for question in eval_questions:
    with tru_recorder as recording:
        sentence_window_engine.query(question)
        

records, feedback = tru.get_records_and_feedback(app_ids=[])
records.head()
        

import pandas as pd

pd.set_option("display.max_colwidth", None)
records[["input", "output"] + feedback]
        

tru.get_leaderboard(app_ids=[])
        

tru.run_dashboard()        

Conclusion

The RAG triad emphasizes balancing recall to capture all relevant information, accuracy to ensure correctness, and granularity to capture detail, aiming for a comprehensive and reliable evaluation of models.

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