How to Use AI for Research (Without the Hallucinations)

In research, "mostly accurate" is an f-grade. If your AI research assistant makes up a statistic 5% of the time, you have to verify 100% of its work—negating the productivity gains.

The "Hallucination Problem" is the primary reason many think tanks have been slow to adopt generative AI. They've seen the viral stories of AI inventing legal cases or misquoting legislation. But for those of us building AI for research, hallucinations are a solvable engineering challenge, not an inevitable byproduct.

The Cure: Retrieval Augmented Generation (RAG)

Hallucinations happen when an AI is asked to "remember" something it learned during training. To fix this, we move the AI's "memory" out of its brain and into a controlled database. This is a Verified AI Knowledge Base.

When you ask Answerable a question, the AI doesn't try to remember the answer. Instead, it searches your uploaded documents for the relevant facts. It then uses the LLM only to synthesize those specific facts into a readable response. If the facts aren't in your documents, the system is instructed to say "I don't know."

Three Steps to Hallucination-Free Research

1. Set Temperature to Zero: This makes the AI predictable and literal. It stops the model from being "creative" with your data.
2. Enforce Strict Citations: We mandate that every sentence generated must have a supporting chunk from the source material. No link, no text.
3. High-Quality OCR: Many hallucinations stem from the AI misreading a poorly scanned PDF. Our AI Readiness Checklist ensures your data is clean before it's indexed.

By following these protocols, Answerable provides a level of rigor that allows researchers to trust the machine. Try it for yourself with our live demo.