How to Stop AI from Mislabeling Inference as Fact
AI research agents often mix facts with guesses. A web page might state a market value. The agent then concludes the market is growing fast. Both statements look the same in the final text. This blend of data and opinion is dangerous.
You cannot fix this with better prompts. Prompts are probabilistic. Under pressure, the model will guess.
The solution is structural. Move the decision from the LLM to your code.
Split the work into two parts:
The LLM does:
- Extract claims from a page.
- Summarize text.
Deterministic code does:
- Score claims.
- Cross-check sources.
- Label claims as FACT or INFERENCE.
- Decide if data is fresh.
A claim earns the FACT label only if it meets strict rules. For example, it must come from two independent sources or one official API. Everything else becomes an INFERENCE.
Use this pipeline:
- PLAN: Turn the question into sub-queries.
- HARVEST: Fetch data from multiple paths.
- NORMALIZE: Use the LLM to extract structured claims. This is the only step using an LLM.
- CORROBORATE: Group claims and count independent sources.
- SCORE: Apply rules to assign labels.
- RENDER: Show facts, inferences, and missing information.
Independence is key. One blog quoting another blog is not two sources. You need distinct domains or an official API to confirm a fact.
Follow these rules for a reliable agent:
- Use escalation: Try a web search first. Only move to a news engine or academic search if the first step fails.
- Track freshness: Label old data as stale. Do not let old facts pass as current.
- Surface gaps: List what you could not find. A silent gap is a failure.
- Ensure reproducibility: The same query must produce the same labels every time. If the labels change, an LLM is scoring the data. Replace that LLM call with a function.
This method lets the model do what it does best: read and extract. It prevents the model from deciding what is true.
Optional learning community: https://t.me/GyaanSetuAi