Using generative AI like Claude for data analysis is unreliable, as the models often miscalculate or 'hallucinate' data, even with clear prompts. To use these tools safely, you must repeatedly instruct the AI to check its work, then perform your own thorough validation before trusting the output.

Anthropic's Claude Code team reports that AI agent skills designed for "verification"—teaching an agent to test and validate its own output—provide an extremely high return on investment. This suggests that building reliability and correctness into AI workflows is as critical, if not more so, than the initial generation capability.

A powerful, practical use of AI in investment research is to verify management's track record. By feeding all historical earnings call transcripts into a large language model, an analyst can quickly ask whether management's past promises and guidance materialized, automating a crucial but time-consuming due diligence step.

LLMs often get stuck or pursue incorrect paths on complex tasks. "Plan mode" forces Claude Code to present its step-by-step checklist for your approval before it starts editing files. This allows you to correct its logic and assumptions upfront, ensuring the final output aligns with your intent and saving time.

Coding agents are becoming powerful tools for general knowledge work. A non-technical user was able to point Claude Code at a data file and have it autonomously produce five complete, well-designed HTML dashboards and analysis reports.

Journalist Casey Newton uses AI tools not to write his columns, but to fact-check them after they're written. He finds that feeding his completed text into an LLM is a surprisingly effective way to catch factual errors, a significant improvement in model capability over the past year.

AI tools like Claude Code are evolving beyond simple SQL debuggers to augment the entire data analysis workflow. This includes monitoring trends, exploring data with external context from tools like Slack, and assisting in crafting compelling narratives from the data, mimicking how a human analyst works.

A powerful and simple method to ensure the accuracy of AI outputs, such as market research citations, is to prompt the AI to review and validate its own work. The AI will often identify its own hallucinations or errors, providing a crucial layer of quality control before data is used for decision-making.

Instead of solely focusing on AI fallibility, a major application is using AI agents to audit human work. Perplexity's "Final Pass" feature analyzes documents for factual errors and internal inconsistencies, finding glaring mistakes in things like Gartner's earnings press releases and work done by professional accountants.