Concept
Most bad answers are missing-context problems, not prompt problems.
The model answered correctly given what it could see. It just could not see enough.
Concept
Context Engineering
Learn to assemble the right context — instructions, data, history, tools — for every model call. Free, one sitting.
Start Quick LessonFree to explore · ~3 minutes
Why this matters
Prompt tricks stopped being the differentiator once models got good at following instructions. What separates a reliable AI feature from a flaky one is context: the right documents, the right history, the right tool results, assembled at the right moment and trimmed before they overflow.
What you'll cover
The Skill That Replaced Prompting
- Why prompt tricks stopped mattering
- Defining context engineering
- The bottleneck moved
- What good looks like
- The discipline in one page
What Goes Into Context
- Instructions
- Retrieved data
- Conversation history
- Tool definitions and results
- Competing for one window
Retrieval and Selection
- Deciding what to retrieve
- How much is enough
- Ordering what you include
- Filtering by permission
- Freshness and recency
Compaction and Memory
- When to compact
- What must survive
- Summarising safely
- Persistent memory
- Stale memory risks
Diagnosing Context Failures
- Missing context, not bad prompts
- Reading what the model received
- Contradictions in context
- Overflow and truncation
- A debugging checklist
What you'll understand
- Assemble context deliberately rather than accidentally
- Decide what to retrieve, keep and drop
- Compact long history without losing the thread
- Diagnose failures caused by missing context
A look inside
Three moments from this Quick Lesson
The real thing — not a mockup of it.
How it fits
Instructions + Retrieved data + History + Tool results → Window
Every one of these competes for the same finite space. Budget them.
Apply
An answer ignores a key document. Most likely cause?
The prompt was not polite enough
The document never made it into context
The model is too small
How it works
01
Understand the concept
A plain-language walkthrough of the idea itself, no prior context assumed.
02
See it in practice
A simple diagram or example showing how it actually fits together.
03
Apply what you learned
One quick check that you can recognise it, not just recall it.
Useful for
DevelopersProduct managersAI engineersFounders
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Frequently asked
Is this the same as prompt engineering?+
It is the larger discipline that absorbed it. Wording is one small input; what information is present matters far more.
What will I learn?+
How to assemble, budget and compact the context around a model call so answers stay reliable.
Is this Quick Lesson free?+
Yes. Quick Lessons are free, short, and do not require a paid plan — sign in only to save your progress.