How To Maintain Chatbot NLP Accuracy Over Time

LLMs are great for data processing, but using new techniques doesn't mean you get to abandon old best practices. The precision and accuracy of LLMs still need to be monitored and maintained, just like with any other AI model. Tips for maintaining accuracy and precision with LLMs: • Define within your team EXACTLY what the desired output looks like. Any area of ambiguity should be resolved with a concrete answer. Even if the business "doesn't care," you should define a behavior. Letting the LLM make these decisions for you leads to high variance/low precision models that are difficult to monitor. • Understand that the most gorgeously-written, seemingly clear and concise prompts can still produce trash. LLMs are not people and don't follow directions like people do. You have to test your prompts over and over and over, no matter how good they look. • Make small prompt changes and carefully monitor each change. Changes should be version tracked and vetted by other developers. • A small change in one part of the prompt can cause seemingly-unrelated regressions (again, LLMs are not people). Regression tests are essential for EVERY change. Organize a list of test case inputs, including those that demonstrate previously-fixed bugs and test your prompt against them. • Test cases should include "controls" where the prompt has historically performed well. Any change to the control output should be studied and any incorrect change is a test failure. • Regression tests should have a single documented bug and clearly-defined success/failure metrics. "If the output contains A, then pass. If output contains B, then fail." This makes it easy to quickly mark regression tests as pass/fail (ideally, automating this process). If a different failure/bug is noted, then it should still be fixed, but separately, and pulled out into a separate test. Any other tips for working with LLMs and data processing?

Stop asking LLMs to "check for accuracy." >> Make the models work instead. There are ways to improve the accuracy of chatbot answers. Instead of accepting it's initial output, you can force it to reevaluate its work in meaningful ways. You can get to truth by forcing your LLM to transform, not give a wink and a nod to the answer it already generated. Have it reprocess your draft. And provide evidence. Some sweet tactics you can try: 🔹Rebuild: "Recreate this answer from fresh sources only. Return what changed." 🔹Cite everything: "Attach a source and short quote after every claim." 🔹Diff it: "Compare the rebuild to the original. List conflicts and missing pieces." 🔹Justify: "For each bullet, add ‘Because: [evidence] >> [claim]’." 🔹Expand: "Add 1 example, 1 edge case, 1 failure mode for each item." 🔹Pros and cons: "Give tradeoffs for each. Note who benefits and who loses." 🔹Disprove: "Try to falsify each point. Provide counterexamples." 🔹Contradiction scan: "Find claims that conflict with each other." 🔹Freshness check: "Verify dates, versions, and timelines. Flag anything stale." 🔹Triangulate: "Give 3 independent passes, then merge them with a rationale." 🔹Referee mode: "Score another LLM’s output with a rubric and evidence." Try using multiple LLMs to cross-check each other. Bottom line: don’t ask "Accurate?" Make the model to work.

Taming AI/LLM Drift - We've all seen sci-fi movies where AI systems become dangerous or turn against humans. While it makes for good entertainment, businesses must take the risk of "AI drift" seriously as real-world AI and LLMs adoption grows. What I mean by drift is when an AI slowly starts behaving in ways it wasn't originally designed to do. The changes can be subtle, but the AI (LLM model) digresses further from its intended purpose over time. A recent example is ChatGPT. Many users have noticed that the OpenAI chatbot gives more generic or questionable responses now compared to its early days. The drift likely comes from it trying to satisfy diverse users. Without moderation, it slowly deviates. So how can we prevent this or at least be aware of it? Here are a few common sense tips: First, evaluate its decisions regularly to catch any funky stuff early. Logging its activity makes monitoring easier. If inconsistencies pop up, it’s time to intervene (always have a human in the loop). Second, refresh the AI’s training data and algorithms to re-align it with desired behaviors (perhaps use different LLM models). And third, by having people double-check the AI’s work and give feedback, you can correct bad calls and keep the system grounded in reality. The key is being vigilant about monitoring your LLM after launch. It takes ongoing governance. But with the proper human oversight, we can harness these models safely while avoiding uncontrolled drift. What steps have you taken to keep AI/LLM accountable? What checks and balances work for your organization? Share your expertise! #ai #chatgpt #drift #artificialintelligence #llms #largelanguagemodel