jasontang-ai/Context-Engineering resource
"Context engineering is the delicate art and science of filling the context window with just the right information for the next step." — Andrej Karpathy. A frontier, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization. observed · 2026-08-28
Health v2 · maintenance only
49/100
- Activity 69
- Release rhythm 35
- Longevity 30
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 431
- days_rel: n/a
- days_push: 187
- n_releases_24m: 0
Adoption not part of the score
9228 stars · 1029 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A first-principles handbook and course on context engineering — designing, orchestrating, and optimizing the full information payload given to LLMs beyond simple prompting. It combines visual explanations, research paper summaries, templates, and agent command integrations for tools like Claude Code.
Use cases
- learn context engineering for llms
- move beyond prompt engineering to context design
- understand how to fill an llm context window effectively
- study research on context optimization for large language models
- find templates and patterns for building llm agents
- take a structured course on context engineering
When to choose
- you want a conceptual, research-grounded education in context design rather than a production library
- you are designing prompts, memory systems, or multi-agent context flows and want patterns and templates
- you want curated links to recent context-engineering research papers
When to avoid
- you need a production-ready SDK or runtime framework with stable APIs
- you want a simple prompt library without theory or coursework
- you need guaranteed stability — the material is explicitly frontier and under active construction
Facets
learning-resource · maturity active
prompt-engineering rag agent-framework documentation large-language-models artificial-intelligence tutorials python cross-platform context-engineering handbook prompt-design llm-context-window first-principles course ai-agents retrieval-augmented-generation
4 sources
- readme: https://github.com/jasontang-ai/Context-Engineering · fetched 2026-08-28 · 6886b0517948
- homepage: https://deepwiki.com/davidkimai/Context-Engineering · fetched 2026-08-29 · 4781414ab292
- site_page: https://deepwiki.com/davidkimai/Context-Engineering/2-getting-started · fetched 2026-08-29 · e669c52ad8a9
- site_page: https://deepwiki.com/davidkimai/Context-Engineering/10-reference-documentation · fetched 2026-08-29 · 821e2d6d423f
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| jasontang-ai/Context-Engineering | main | 49 |
For agents
markdown · JSON · MCP: product_card(name="jasontang-ai/Context-Engineering")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem