What is context engineering and why should you care?
My inspiration for this post came after coming across
Context Engineering@dbreunig and I did a meetup on context engineering last night. Wanted to share slides (below) + a recap of some themes / discussion points.
— Lance Martin (@RLanceMartin) July 24, 2025
1/ Context grows w/ agents. @ManusAI_HQ mentions typical task requires ~50 tool calls.https://t.co/6cgk2SyaV4
2/… pic.twitter.com/dxPfqLTKbI
Lance shared five context engineering techniques:
- Offload
Move information out of the model’s prompt into external storage such as files, databases, or memory systems. The agent retrieves it when needed instead of keeping everything in the context window.
- Reduce
Compress or remove unnecessary context to keep prompts small and relevant. Common methods include summarization, pruning old messages, and condensing tool outputs.
- Retrieve
Fetch only the relevant information from external sources at runtime (often via RAG). Instead of sending all knowledge to the model, the system selects the most relevant pieces.
- Isolate
Separate tasks or reasoning into different contexts, often using multiple agents or modules. Each component works with a smaller, focused context to reduce interference and overload.
- Cache
Store and reuse previously computed context, such as prompts, tool results, or message histories. This reduces cost and latency by avoiding repeated computation.
In short:
- Offload → move context out
- Reduce → shrink context
- Retrieve → pull needed context
- Isolate → split context
- Cache → reuse context