AI Engineer @ Microsoft | Building LLM & RAG Apps | Always learning, exploring and sharing the frontiers of AI.
"We're not going straight to a world where AI does everything and engineers disappear. Instead, engineers are shifting rolesโfrom implementers to orchestrators."
"I think something people don't talk enough about when discussing AI agents and AI engineers doing all this stuff for youโฆ is basically we're all becoming engineering managers."
Just finished listening to an incredible podcast featuring an interview with Wu Yi โ a Tsinghua alum and former OpenAI researcher โ and his take on Reinforcement Learning (RL) was one of the clearest Iโve seen!
Wu Yi explains that RL is very different from traditional supervised learning (like image classification). In supervised learning, we train models using a fixed set of labeled data โ one-shot answers.
RL, on the other hand, is more like playing a game: you need to make a sequence of decisions (serve, move, react), and there's no single โcorrectโ path. The quality of your decisions is judged by the final outcome (win or lose). Itโs about multi-step decision-making โ much closer to how the real world works.
Key Takeaways:
Prompt engineering is an ๐ถ๐๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐ involving continuous ๐๐ฒ๐๐๐ถ๐ป๐ด, ๐บ๐ผ๐ฑ๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป, ๐ฎ๐ป๐ฑ ๐ผ๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป. As models continue to evolve, prompts must adapt accordingly to maintain effectiveness.
Prompt engineering is about "communicating" with LLM in a way that maximizes the model's understanding and performance on a given task. At its core, prompt engineering involves designing, refining, and optimizing the text inputs (prompts) given to models to elicit accurate, relevant, and useful responses.
"Agent" can be defined in several ways. Some define agents as fully autonomous systems that operate independently over extended periods, using various tools to accomplish complex tasks. Others use the term to describe more prescriptive implementations that follow predefined workflows. At Anthropic, they categorize all these variations as agentic systems, but draw an important architectural distinction between workflows and agents:
Below, we will explore both types of agentic systems in detail.
In todayโs fast-paced development landscape, automated browser testing and web scraping are more vital than ever. Three of the most prominent tools in this space are Selenium, Puppeteer, and Playwright. Each has its unique strengths and target use cases. In this post, weโll explore how they stack up in terms of browser support, language options, performance, ease of use, and more.
However, for the most powerful and reliable option, Playwright is the best of the three.
Define test cases to ensure you're actively improving your app & not causing any regressions.
Break down one LLM call into multiple
AI systems do a lot better when you have many LLM calls chained together. i.e, instead of sending an LLM call to a model to generate code, send it to a "architect" model to generate a plan, then a "coding" model to generate code, then a "reviewer" model to verify.
Start simple (with 1 LLM call)