How to reduce AI hallucinations about your brand

Keep interventions, hypotheses, assets, observations and decisions in a searchable workspace. Record what is known and what still needs checking.

You cannot guarantee an AI model never to be wrong. You can reduce brand-specific errors by supplying current, sourced facts; retrieving only relevant context; requiring uncertainty when evidence is missing; and reviewing important claims before publication or action.

Identify the type of error

“Hallucination” often hides several problems:

• the model invented a feature or customer;
• memory contains an obsolete price or plan;
• two sources contradict each other;
• retrieval returned the wrong product or market;
• a true statement lost its condition;
• the prompt requested certainty where evidence was absent;
• a third-party opinion was presented as an official brand fact.

Classifying the cause determines the fix. Rewriting the prompt will not repair obsolete source data.

Create a hierarchy of truth

Define which sources prevail: approved product records and current documentation usually outrank old presentations, drafts or generated summaries. Store source, owner, last verification date, applicable market and status with important claims.

The brand-memory guide explains how to archive contradictions instead of blending them.

Structure claims and conditions together

For every product, record current capabilities, requirements, proof and explicit non-capabilities. The guide to teaching AI about products and services provides a schema.

Retrieve less, but retrieve the right context

Sending the entire knowledge base can introduce noise and conflicts. Retrieve by product, market, task and date. Return enough surrounding context to preserve conditions. Inspect retrieved passages during testing rather than evaluating only the fluent final answer.

If the wrong record appears, improve metadata, chunk boundaries or source status. Do not compensate with increasingly long instructions.

Give the model an evidence contract

For important work, instruct the assistant to:

1. use only retrieved or supplied facts for brand-specific claims;
2. cite or identify the supporting record;
3. distinguish fact, observation and hypothesis;
4. state “not verified” when evidence is missing;
5. surface contradictions instead of resolving them silently;
6. request approval before writing or publishing high-impact changes.

This behavioural rule belongs in the task or system prompt. Current facts belong in memory. See brand memory versus a long system prompt.

Test with an adversarial question set

Create questions likely to expose mistakes:

• What does the product cost?
• Which integrations are currently supported?
• Does it publish automatically?
• Which customer results can it prove?
• Is a provider included or billed separately?
• Which markets and languages are active?

Include questions whose correct answer is “unknown” or “not supported”. A system that always produces a confident answer fails this evaluation.

Review outputs by risk

Low-risk brainstorming can tolerate wider generation. Prices, legal statements, security, customer claims, compatibility and publication need stronger evidence and human approval. Use automated checks to find unsupported sentences, but review the source behind every material claim.

Maintain memory after product changes

When an offer changes, update the canonical record, search for conflicting knowledge and review published pages. Mark history as historical rather than deleting context needed for audits. Re-run the adversarial test and inspect retrieval.

An obsolete article can reintroduce the wrong fact through crawling even after internal memory is fixed. Product truth and public content need the same migration plan.

Frequently asked questions

Can RAG eliminate hallucinations?

No. Retrieval can ground answers, but sources may be wrong, retrieval may fail and the model can misinterpret context.

Should we forbid the AI from answering when unsure?

For high-risk brand facts, require a verified source or an explicit uncertainty response. The threshold can be lower for brainstorming.

Is more knowledge always safer?

No. Duplicates, stale records and irrelevant documents increase conflict and retrieval noise.

Does a citation prove the claim?

No. Verify that the cited passage directly supports the statement and is authoritative and current.

Make “I don't know” an acceptable result

Accuracy improves when the system has governed sources, selective retrieval, explicit uncertainty and review proportional to risk. The objective is not confident prose; it is a claim that can be traced and corrected.

What BeKnow keeps

Keep interventions, hypotheses, assets, observations and decisions in a searchable workspace. Record what is known and what still needs checking.

A change in performance after an intervention does not prove that the intervention caused it. Cross-platform attribution and a complete analytics dashboard are not available today.

Project memory and data imports do not require an AI model key. A compatible external AI client may have its own costs. BYOK applies only to available functions that actually call an external provider.

Next step

Start with one project, one documented change and the evidence needed to review it. Source connections.

Record your first intervention. How it works.