Five Tactics to Improve AI Accuracy and Trust

Establishing Semantic and Data Precision

To prevent AI from guessing or hallucinating, users must eliminate ambiguity in their prompts. When using terms like "top customers," define the metric explicitly (e.g., "highest revenue clients in the last 12 months"). Users should instruct the AI to identify and define ambiguous terms before generating an answer. Additionally, when working with file-based agents, maintain a single source of truth by archiving outdated versions (e.g., v1, v2, final) to prevent the model from retrieving incorrect data.

Verifying and Auditing Outputs

For information extraction tasks, mandate that the model provides a "receipt" for its work. This involves instructing the AI to cite the specific file, page, or section for every claim. If the model infers a value, it must explicitly flag the inference and suggest a specific location for the user to audit. For high-stakes tasks involving financial, legal, or reputational risk, use a two-step verification process. First, challenge the output in a fresh chat session to bypass the model's tendency to defend its initial response. Second, cross-verify the output by passing it to a different model (e.g., GPT) to see if both reach the same conclusion.

Benchmarking for Automation

Before automating a process end-to-end, validate the model's performance using a test set of 10 samples where the input and correct output are already known. If the model achieves 10/10 accuracy, it is likely safe for automation. For scores between 7/10 and 9/10, refine the system instructions, provide additional context, or upgrade to a more capable model. If the model scores below 7/10, archive the use case and re-test it only when a new, more capable model is released.