Is Your LLM Lying? A Practical Guide to Building Trustworthy AI

At AWS Community Day Manila 2025, I got the chance to talk about something that keeps many AI engineers up at night: how do you know if your LLM is actually telling the truth? The talk focused on building AI systems you can trust using AWS Bedrock's built-in tools for evaluation, guardrails, and monitoring.

Why This Matters

LLMs are incredibly powerful, but they have a problem. They can confidently make things up. When you're building production systems, this isn't just annoying, it's dangerous. The talk was all about showing practical ways to catch these issues before they reach your users.

Evaluating Your Models

I showed how to set up proper evaluations using AWS Bedrock. We covered baseline evals first, where you test the model in a general sense. There are two approaches here: programmatic checks (like regex or format validation) and using another model as a judge. The model-as-a-judge approach is surprisingly effective for checking things like tone, accuracy, and relevance.

Then we got into RAG evals, which are more specific. Here you need a golden dataset with example prompts and the actual knowledge base. The model judges whether the responses are accurate based on what it should know. Different task types need different metrics, and I walked through several examples showing what to measure for classification tasks versus generation tasks versus retrieval tasks.

Guardrails Are Your Safety Net

This was probably the most important part of the talk. Guardrails help you catch problems in real-time, both on the way in (user prompts) and on the way out (model responses). I focused on two key types: context relevance (is the retrieved info actually relevant?) and grounding (is the model sticking to the facts or making things up?).

The demos showed how to set these up in the AWS console and what happens when they trigger. It's like having a quality control checkpoint before anything reaches production.

Managing Your Prompts

Prompt management in Bedrock is underrated. Instead of having prompts scattered across your codebase, you can version them, test them, and roll them back if something breaks. I showed how this makes it way easier to iterate on your AI features without worrying about breaking production.

Monitoring What Matters

You can't improve what you don't measure. I showed how important it is to have model invocation logging in Bedrock, automatic log storage to S3, and performance dashboards. The key is tracking the right metrics over time so you can catch quality degradation before users complain.

Production Release Gates

One of the practical examples I shared was setting up release gates. Things like: baseline correctness needs to hit 80%, RAG faithfulness needs to be above 90%, guardrail trigger rates should stay under 5%. These give you concrete numbers to hit before shipping updates.

The best AI systems aren't the ones that never fail. They're the ones that know when they might be wrong and handle it gracefully.

Try It Yourself

The whole talk was pretty hands-on with AWS console demos. I wrapped up by encouraging everyone to take what they learned and try integrating Bedrock's SDK or API into their own eval stacks. The tools are there and they're easier to use than building everything from scratch.

If you're building with LLMs and want to sleep better at night knowing your system won't hallucinate wild stuff to users, these patterns really help. Start with evals, add guardrails, and monitor everything. That's the path to trustworthy AI.