Into the context window
Only what the task needs right now, loaded at runtime, sharp and relevant.
Craft · Context engineering
Context engineering
Most AI projects fail on context, not on the model. This pillar keeps context structured and high-quality as it grows, deciding what belongs in it, and maintaining the knowledge layer before it rots.
§01 Craft
What belongs in context, and what doesn't
Not everything belongs in the context window, and almost nothing belongs in training. The first engineering decision is the boundary: which knowledge is loaded as context at runtime, which lives in a searchable knowledge layer, and which the model never needs to see. Pour everything in and you dilute what matters, making every answer costlier and blurrier. Give too little and you get confident gaps.
Into the knowledge layer
Searchable and versioned, retrieved deliberately when needed.
Not at all
What the model never needs to see stays out, deliberately.
§02 Craft
Preventing context rot and poisoning
Context that grows will rot, unless someone maintains it. Stale facts, contradictory rules, and a single wrong entry that bleeds into every answer (context poisoning) are not edge cases; they are the default state of unmaintained systems. We treat context like code: versioned, with clear provenance per entry and a process that removes what is wrong before it multiplies. That keeps the knowledge layer high-quality as it gets larger.
- 01 Every entry carries provenance
- 02 Versioned like code
- 03 Reviewed on a schedule
- 04 Wrong entries removed before they spread
Human AI Algorithm
§03 Craft
Staying sharp as tools and models change
A prompt that runs reliably today can break at the next model update. Tools get replaced, models get swapped, the business shifts, a knowledge layer frozen in a forgotten document ages faster than it helps. We keep prompts, rules, and project context in a structured, searchable layer that grows with you and gets re-sharpened on a schedule. So the same prompt returns the same quality next week, not a different answer.
The same prompt returns the same quality next week, not a different answer.
§04 The problem
Why context is the bottleneck.
Most AI projects fail on the wrong context. AI tools are powerful, but they only deliver when they receive the right context, instructions, data, and verification. Companies that skip this step do not get bad AI, they get unpredictable AI: the same prompt returns a different answer next week, sensitive data lands in the wrong tool, outputs look convincing but carry quiet errors. Trust erodes quietly.
- 01 Inconsistent AI outputs across teams DRIFT
- 02 Unclear or unmaintained prompts FRAGILE
- 03 Missing business and project context HOLLOW
- 04 Repeated mistakes nobody owns REWORK
- 05 Unsafe data usage and shadow tooling RISK
- 06 No verification or review process UNCHECKED
- 07 Low trust from employees and managers CULTURAL
The fix is not another tool. It is structure.
§05 Read on
More on context engineering
How context stays structured, and how it quietly fails, we go deeper in the blog and in our docs. mema is the open research behind it.