Mudra Mukherjee, Ph.D.’s Post

If you're in the gen AI space, give this a read. It's accessible yet illuminating, and is a perfect guide to the real challenges of building LLM based applications

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Where to start in AI as a software engineer? Learn the basic concepts of AI models, study different models with different purposes, have a better idea about differences among them, and then start addressing using some models on your local machine or environment. Learn how to develop simple solutions around AI models and grow up the sophistication utilizing advanced concepts such as Agents and RAG. Figure out different use cases and try some such as text, voice, vision, images, and audio. There are piles of potential use cases in each category, and even you may combine multiple categories together. Take your chances and be creative, there are no rules yet in this domain.

Does keeping up with the latest in AI make you feel like Sisyphus pushing his boulder? Here’s a decision-making framework I use to decide what to skim and where to go deep: Relevance to My Work: As Head of AI at Amperity, I prioritize advancements that affect: 1. Personalization of customer experiences 2. Workflows of data analysts and engineers 3. Software engineering practices If the problem being solved isn't directly relevant to these areas, I opt for a quick summary of the news. Durability of the Advancement: I focus on techniques that will stand the test of time, and I invest less energy in those that are likely to evolve. For example, passing the right information to LLMs is a problem likely to persist, though specific techniques for solving this problem may shift (fine-tuning, few-shot prompting, RAG, etc.). New Possibilities: I always ask myself, "What can we do now that wasn't possible before? Simple enough, but this framework definitely helps me keep up without getting overwhelmed. Plus, you could always, you know, use AI to summarize the AI news for you… 😉

Great post by LinkedIn on ins and outs of building a GenAI product. Many has this perception that LLM can solve all engineering and AI problem. It probably can but with a lot of engineering efforts, as shown in the post. The first time I built a GenAI application I was so excited that I got an “almost there” version immediately, without much effort. Naively I thought I could easily get to the “this is what I want” version in a few days. This turned out to be a continuous effort for a couple of weeks. Just like the post mentioned the first 80% is easy to achieve and to bring it to surpass a 95% requires a much longer time frame. And this is a point that many enterprises (sometimes even developers) do not realise. Just like traditional ML applications, building with LLM also requires technicalities which might look different from what we usually do. Not forgetting the prompting part takes many trials to get to a stable state. If you are building with LLM or Gen AI or just want to know the good and the ugly, go through this post and let me know which points resonated with you! #llm #generativeai #largelanguagemodel #promptengineering

Very insightful article on building products with LLMs. "For product experiences that tolerate such a level of errors, building with generative AI is refreshingly straightforward. But it also creates unattainable expectations, the initial pace created a false sense of ‘almost there,’ which became discouraging as the rate of improvement slowed significantly for each subsequent 1% gain." The key is to knowing which tool (agents, RAG, self-correction, etc.) can help you overcome hurdles and make small progress once you get past the first 80% of the way. https://lnkd.in/eQ6WU48m

Interesting article on building Generative AI Products. It talks about practical challenges when deploying GenAI in products. Also, insights into unattainable expectations in terms achieving consistent quality and the time required to do so.

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