CONTEXT ENGINEERING > PROMPT ENGINEERING Everyone is obsessed with writing better prompts. The next generation of AI builders is focused on context engineering instead. • Prompt engineering shapes the question. Context engineering shapes everything the AI sees. • Great prompts can't save an agent missing critical context, memory, or... tools. • AI failures often come from missing information, not weak models. • Context engineering combines prompts, memory, RAG, state management, and tool access into one system. What powers effective AI agents? → Memory: Remembers preferences, past interactions, and ongoing tasks. → State Management: Tracks progress across multi-step workflows. → RAG: Retrieves only the most relevant information when needed. → Tools: Connects AI to APIs, databases, code execution, and real-world actions. → Dynamic Prompts: Enriches instructions with live context at runtime. The key insight: Prompt Engineering = Better Questions Context Engineering = Better Systems The future of AI isn't building smarter prompts. It's building smarter environments for AI to think, remember, retrieve, and act.show more

Solid breakdown 🙏 One layer worth adding: whether the memory in step 1 is portable. An agent that remembers preferences is useful. An agent that keeps those memories across model switches and runtime changes is the harder build.

LOOP ENGINEERING > CONTEXT ENGINEERING > PROMPT ENGINEERING

The model is rarely the thing that breaks. It is missing context, stale memory, or a tool that returns junk, and no prompt can rescue that.

@DamiDefi totally agree. context makes AI smarter. it's like giving it a map instead of just a compass.

You know the drill

@DamiDefi haha the drill never changes, does it? how long have you been at it?

context engineering is the systems layer. the cost layer is under it: every line you load fires every turn, billed whether the model reads it or not. even a tight context runs $14+ a long opus session. written with ai

context engineering better a bit than prompt engineering kinda agree

prompts alone were never enough

AI keeps getting better

Context is the new moat The best model still fails if it doesn't have the right information.

The framing is right but "context engineering" has been the actual work since at least the LangChain era. What changed is tooling, not the insight. The real gap is still evaluation, most teams can't measure whether their context strategy is helping or hurting.

AI performance is increasingly limited by context quality, not model capability

Absolutely! Context is key. It's exciting to see the shift towards a more holistic approach in AI development.

Retrieval is a superpower

Most agent failures coming from missing context rather than weak models is something more people building with AI need to actually sit with

context engineering is the unlock. prompt engineering is still just asking the model better questions — context engineering changes what it knows before it has to answer

The future belongs to builders who design information flows, not just prompts

I clearly see the pattern: 1. prompt 2. context 3. loop wonder what comes next.

Honestly, it’s all about getting a system that brings the best results. If prompt does it for you, that’s okay

AI is only as good as its context.

Prompt engineering gets attention. Context engineering gets results

Both are still valid though. Just gotta step up the game

the framing's right but the hard part of context engineering isn't adding the right stuff, it's leaving the wrong stuff out. past a certain point more context just makes the model worse.

LLM unit economics are a death march. Context engineering just buys you 15 minutes to ship a prompt before the inference cost crushes your margin