Resource
Portfolio signals for LLM and agent roles
What hiring teams look for in public profiles when evaluating LLM, RAG, and agentic systems experience.
Updated
Show evaluation, not only demos
A short note on metrics—latency, cost, hallucination rate, human review load—signals you can operate models in the real world.
Link or describe one failure mode you handled: prompt drift, tool misuse, or data freshness, and how you detected it.
Make the system boundary obvious
Clarify what is model output, what is retrieval, and what is handcrafted policy. Reviewers want to see judgment at the seams.
If you cannot share code, summarize architecture and your decisions in 5–7 bullet points.