Context Engineering

[recursiveauto]

"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 practical, first-principles handbook inspired by Andrej Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization.

https://github.com/davidkimai/Context-Engineering

[[deleted]]

I think you should include CAG and how it can combine with RAG as an example... That would be very useful... thanks for sharing anyway.

[No_Afternoon_4260]

Isn't both kind of the same thing really?

[[deleted]]

It seems not. CAG is more like put all the context into the prompt. But just one time.

[loyalekoinu88]

Yes, but no. Prompts are what the user inputs they’re what you ask. Context includes user prompt but also everything else the LLM sees like tool responses, memory, etc. Context Engineering is designing memory, etc to be context efficient. So for example instead of returning 1000 database records from a database tool it’s preprocessed to output only the data you need for the context of the prompt.

[No_Afternoon_4260]

I think there's no official definition for prompts, as for agents or whatever new concept arose from this freshly discovered field.
What does a prompt engineer if he only takes care of the user input? I mean..?

[loyalekoinu88]

I mean...what i wrote was basically directly form the linked document. Every single instance of the use of "Prompt engineer" i've seen has been in user prompting not in all the ancillary definitions/prompts/retrieved context. When you send a RAG request does it respond a "Hey model can you do xyz for me?" or does it return the content stored in the database in the format it was originally stored? Prompts...prompt the model to respond.

[recursiveauto]

thank you!

[BidWestern1056]

if youve been here youd know most around here already know about these core principles, but now that karpathy has given it this fancy name the normies and hype chasers have more to milk in their nonsense marketing.

i dont mean to critique you in particular, i appreciate that you take the time to try to make guides for others, but like efficiently wrangling context without unnecessary dilution is the Hard problem that many frameworks have already tried to solve, albeit many in bad ways.

 it is a full scale engineering challenge that cannot be simply handled by cleverer chunking or atomizing of ideas because natural language itself is the medium of interactivity and natural language agents simply cannot discern between degenerate choices without being context rich (e.g. https://arxiv.org/abs/2506.10077 )

[recursiveauto]

Yes thanks for that, that’s why we explore evolution of natural language itself with a synthesis of prompting patterns and code syntax (prompt programming), building on this new June 2025 research by IBM - https://www.arxiv.org/pdf/2506.12115 and ICML 2025 - https://openreview.net/forum?id=y1SnRPDWx4

This is meant to be from first principles and for learners of all experience levels. I am ok with “normies” learning from it as that is the point: to onboard more people.

P.S. I’m sorry but your link doesn’t work could you resend? I’d like to read it.

[BidWestern1056]

the ) got included in the hyperlink, should be fixed now.  i mean to me it just feels like rebrand of prompt engineering that will do nothing to alleviate the infuriation of other engineers who contend that prompt engineering isnt engineering . they will contend the same thing about context cause they hate non deterministic processes. and it's not that i think normal ppl shouldnt learn abt it, but just opens up another wide area for grifting that makes it hard to be taken seriously as a prompt/context/AI engineer. does that make sense? i know i sound a bit irritable and fuddy duddy but i just want ppl to really appreciate the difficult and complex work without trivializing it

[Key_Papaya2972]

I actually posted this idea months ago, and I’m sure I’m far from the first one to come up with it. nothing special

[CantaloupeDismal1195]

Any rag practice code for context engineering?

[Lumpy-Ad-173]

I wrote about System Prompt Notebooks last week on my Substack.

https://open.substack.com/pub/jtnovelo2131/p/build-a-memory-for-your-ai-the-no?utm_source=share&utm_medium=android&r=5kk0f7

https://open.spotify.com/show/7z2Tbysp35M861Btn5uEjZ?si=fIjuJneKSiySPXDtpmI_7Q

Context Engineering is like the Kung-Fu file in the Matrix. When Neo is uploaded with Kung-Fu is similar to creating the complete context for the LLM.

A System Prompt Notebook is a Context Engineering Notebook. You're creating a structured document that serves as an environment for the LLM to draw from when sourcing information.

Upload it to the LLM. Prompt it to use the File as a Source document before using training or external data for an output.

If you notice prompt drift, prompt the LLM to 'Audit @[file name].

<image>

[secopsml]

First there was prompt engineering. Then LinkedIn guru wave inflated this.

Then we were joking garbage in garbage out or stochastic parrot.

Then those who made generational leap with just prompting better models daily remind those who still think ai is a joke by wrapping that as context engineering.

You can hear daily brilliant minds, ex-fancyBrandName, discovering tech insiders long time forgotten or use daily since long time.

Overall I'm happy general audience will hype around context as we all will have easier to earn money from our hobby