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🪟Learn Context Engineering

Stop polishing the prompt and start engineering the whole context — system instructions, examples, retrieval, history — as a budget you allocate on purpose. By the end you can refactor one bloated context into a prioritized layout and measure whether quality went up.

Applied14 drops~2-week path · 5–8 min/daytechnology

Phase 1Prompts End at the Period; Context Doesn't

Reframe prompting as arranging the whole window

4 drops
  1. The prompt is the smallest part of the prompt

    6 min

    What you call 'the prompt' is one slice of a much larger context window the model actually sees.

  2. Tokens are a budget, not a limit

    6 min

    Every token in the window is competing — for attention, for cost, and for room — so context design is allocation, not addition.

  3. Position is a design decision

    7 min

    Where a token sits in the window changes how much the model weights it — start, end, and middle are not interchangeable.

  4. Five layers, four levers

    6 min

    Every context is built from the same five layers — system, examples, retrieval, history, user — and engineering means tuning each one separately.

Phase 2Audit a Real Call, Token by Token

Audit a real call and label every token

5 drops
  1. Label every token by purpose

    8 min

    Once every token in your context has a labeled purpose, the dead weight becomes obvious.

  2. Cost each section, not just the whole call

    7 min

    The token cost of each labeled section is the data you need to make trade-offs — and almost no one collects it.

  3. Define quality before you tune

    7 min

    If you can't say what 'better' looks like in your context, every refactor will feel correct and prove nothing.

  4. Find the layer that owns the failure

    6 min

    Every recurring failure has a home in the five-layer stack — finding the home is most of the fix.

  5. Run the same input ten times

    7 min

    One sample is an anecdote; ten samples per change is the cheapest evaluation that doesn't lie.

Phase 3Sequence the Window on Purpose

Sequence system, examples, retrieval, history

4 drops
  1. The system prompt that grew teeth

    8 min

    The system prompt that grew teeth

  2. The retrieval order that lied

    8 min

    The retrieval order that lied

  3. The chat that ate its own context

    8 min

    The chat that ate its own context

  4. When examples lie and instructions tell the truth

    8 min

    When examples lie and instructions tell the truth

Phase 4Refactor One Bloated Context

Refactor one bloated context and measure it

1 drop
  1. Refactor one bloated context end-to-end

    25 min

    Refactor one bloated context end-to-end

Frequently asked questions

What is context engineering and how is it different from prompt engineering?
This is covered in the “Learn Context Engineering” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
How do I decide what goes in the system prompt versus retrieved context?
This is covered in the “Learn Context Engineering” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
Where should few-shot examples go in the context window?
This is covered in the “Learn Context Engineering” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
How do I measure whether a context change actually improved quality?
This is covered in the “Learn Context Engineering” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
How do I summarize long conversation history without losing important details?
This is covered in the “Learn Context Engineering” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.