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Context Engineering — The Complete Guide

An MIT-licensed GitHub reference guide that collects research papers, articles, videos, tools, courses and practices about context engineering for large language models, including context windows, prompt engineering and retrieval-augmented generation.

Independent OpenViST project profile, based on linked sources.

Context Engineering — The Complete Guide — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a curated starting point for studying how to select, structure and manage information supplied to an LLM, rather than a ready-to-run context pipeline.

Who it is for

  • Developers learning how retrieval, memory, tools and other context components fit into LLM systems.
  • Researchers and students looking for an organized starting point for context-engineering literature and learning materials.
  • Prompt engineers comparing prompt wording with broader context-pipeline design.

Documented uses

  • Find papers, articles, videos and courses about LLM context engineering.
  • Explore concepts such as token budgets, information ordering, retrieval, memory and relevance filtering.
  • Discover tools, frameworks and techniques related to context-window optimization and RAG.
Before you choose

Requirements & limits

The guide is in a public GitHub repository that declares an MIT license.

  • It is a reference collection, not a hosted API or software implementation.
  • The guide covers several resource formats, including papers, videos, articles, tools, courses and techniques.
  • Its scope includes both prompt engineering and broader system-level context design.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Use the context engineering guide as a research reference

Explore curated materials on context windows, prompt engineering, retrieval-augmented generation, and related tools.

Before you start

  • Access to the public GitHub repository or its README.

Steps

  1. Open the repository README.

  2. Use its table of contents to navigate to a topic such as research papers, videos, tools, techniques, or courses.

  3. Consult the relevant listed materials for further study.

Expected outcome

A navigable starting point for studying context engineering and finding related resources.

Guide limitations

  • The repository is a reference collection, not a software implementation or hosted API.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
mlnjsh/context-engineering · Official homepage title · retained in its source language