AI Dev Skills
Context Engineering
~ Moderate — 5 repos
What is it?
Strategically managing what information goes into an LLM's context window for optimal performance. Context quality determines output quality more than model choice in most real-world cases.
Why it matters for AI PMs
Context window management is the difference between agents that work and agents that hallucinate or loop. Understanding this is critical for debugging and improving AI product quality.
The 2026 landscape
Mem0 and Letta/MemGPT are the leading tools for persistent agent memory. Context compression and retrieval-augmented memory are active research areas becoming production tools.
What strong coverage looks like
Strong context engineering coverage shows a team thinking deeply about agent reliability. They manage context budgets, compress history, and persist important information across sessions.
Your library coverage (5 repos)
Key concepts to know
- •Context window limits and token budgets
- •Sliding window and memory compression
- •Retrieval vs storage tradeoffs
- •KV cache optimization
- •Long-context models and their tradeoffs