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Foundations

Your AI needs the right context.

A bigger prompt is not a substitute for deciding what belongs in it.

Context is what the task needs

A customer question may require the current order, the applicable policy and an earlier commitment. The entire company archive is not equally useful.

Too little context loses essential facts. Irrelevant or conflicting material can confuse the answer. The task is to select a useful working set and keep a route back to its sources.

Selection has several steps

First establish the situation: what is being done, for whom and under which constraints. Find eligible material, rank its relevance and fit it into the available prompt. Search original history when a precise detail is needed.

Access belongs before use. A highly relevant private fact is still ineligible for an audience that cannot receive it.

Cognition does more than retrieve

After retrieval, the model still has to weigh what it found. An old preference may conflict with a new deadline. A confident statement may have a weak source. A recommendation may require information that is missing.

A useful system can surface the conflict or ask for evidence. Similarity to the question is not the same as authority to decide.

Measure what reaches the answer

Inspect which context was supplied, what was dropped and whether the result used the evidence correctly. Count failed lookups as failures even when the search request returned normally.

thinqOS separates selected Mind context from episodic Memory evidence. Our replay study explains why this direction is worth testing and what that study did not establish.

If you already have a knowledge base

An existing map of entities, relationships and definitions is useful material, not something to throw away. The question is whether it should sit underneath the layer your agents actually query, rather than being the thing they query directly.

Treat it as a source that feeds beliefs and gets checked against evidence, not a store that is automatically current just because it was modeled once. Ask whether it can represent confidence, whether two identities can hold different views of the same fact, and whether the structure can leave the platform that built it.

Continue exploring

Put the idea in context.

Does giving AI more history improve its answers?

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Answers grounded in the thinqOS website, with a page-search fallback
Current contextThe thinqOS website

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thinqOS tries to answer with cited sources. If it can't, you'll see relevant pages instead - never presented as an AI answer.

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