Context Engineering

Learn how to get the most out of the context window in Amp, with the least amount of work.

Karl Marx once wrote that ``the human essence is the ensemble of social relations'', suggesting that individuals are not isolated entities but are fundamentally shaped by their interactions with...

Manus approaches to context engineering.

The blog explores how to apply practical context engineering techniques using Agno to build AI agents that are faster, more efficient, and better at collaboration. It covers core techniques that include crafting precise system messages, selectively managing context to reduce token use, applying few-shot learning to teach behavior, and coordinating multi-agent teams effectively.

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

Taking care of your context is the key to building successful agents. Just because there’s a 1 million token context window doesn’t mean you should fill it.

How to test agent ideas in hours using Claude Code SDK without building orchestration infrastructure - proven methodology from consulting engagements

Applications of reinforcement learning (RL) in AI model building has been a growing topic over the past few months. From Deepseek models incorporating RL mechanics into their training processes to...

This post shares the local optima Manus arrived at through our own "SGD". If you're building your own AI agent, we hope these principles help you converge faster.

LlamaIndex is a simple, flexible framework for building knowledge assistants using LLMs connected to your enterprise data.

Context Engineering Guide By DAIR.AI Academy Table of Contents What is Context Engineering? Context Engineering is Action System Prompt Instructions User Input Structured Inputs and Outputs Tool Calling RAG & Memory State & Historical Context Advanced Context Engineering Resources What is Co...

Context Engineering is the new skill in AI. It is about providing the right information and tools, in the right format, at the right time.

TL;DR Agents need context to perform tasks. Context engineering is the art and science of filling the context window with just the right information at each step of an agent’s trajectory. In this post, we break down some common strategies — write, select, compress, and isolate — for context engineering

Discover 8 powerful tools transforming prompt engineering from trial-and-error into scalable systems—featuring visual workflows, auto-tuned prompts, and memory-

Learn how Chain-of-Thought prompting improves AI reasoning by guiding models to explain their thought process. Discover its impact on LLM accuracy and complex tasks.

We introduce THINK-AND-EXECUTE, a framework that performs reasoning with a pseudocode that contains the common logical structure of a given task.

Quickly understand inscrutable LLM frameworks by intercepting API calls.

Use Prompt Canvas in LangSmith to collaborate with an AI agent to build and optimize your prompts.

Structured input/output, prefilling, n-shots prompting, chain-of-thought, reducing hallucinations, etc.

Step by step guide to learn Prompt Engineering. We also have resources and short descriptions attached to the roadmap items so you can get everything you want to learn in one place.