Evals are critical, and I love the practicality of this guide!
One problem not covered here is: knowing which data to review.
If your AI system produces say 95% accurate responses, your Evals team will spend too much time reviewing production logs to discover different AI failure modes.
To enable your Evals team to only spend time reviewing the high-signal responses that are likely incorrect, I built a tool that automatically surfaces the least trustworthy LLM responses:
You might be thinking of LLM as-a-judge, where one simply asks another LLM to fact-check the response. Indeed that is very unreliable due to LLM hallucinations, the problem we are trying to mitigate in the first place.
TLM is instead an uncertainty estimation technique applied to LLMs, not another LLM model.
This is why I built a startup for automated real-time trustworthiness scoring of LLM responses: https://help.cleanlab.ai/tlm/
Tools to mitigate unchecked hallucination are critical for high-stakes AI applications across finance, insurance, medicine, and law. At many enterprises I work with, even straightforward AI for customer support is too unreliable without a trust layer for detecting and remediating hallucinations.
Curious to learn how much harder it is to red-team models that use the second line of defense of an explicit guardrails library that checks the LLM response in a second step. Such as Nvidia's Nemo Guardrails package.
I'm excited for LLM applications that can setup, monitor/validate, and optimize data pipelines at scale. Seems possible soon given that SQL and most data records aren't intended to be human-friendly
Here are some benchmarks I ran that compare the precision/recall of various LLM error-detection methods, including logprobs and LLM self-evaluation / verbalized confidence:
There is however a subfield of statistical ML of model uncertainty quantification. I've developed a product by applying to it to LLMs that can score the trustworthiness of any LLM response. Like any ML-based product, my tool is not perfect, but it can detect incorrect LLM responses with pretty high precision/recall across applications spanning RAG / Q&A, data extraction, classification, summarization, ...
To try out an existing product that quantifies LLM uncertainty (accurately incorporating both aleatoric & epistemic uncertainty), you can try this Trustworthy Language Model I built (after similar research):
New feature alert: Auto-train & deploy reliable ML models (more accurate than fine-tuned OpenAI LLMs) on messy real-world data — all in just a few clicks!
Common reasons companies struggle to quickly get good ML models deployed and generating business value: messy data full of issues, a need to explore many ML models to train a good one, and infrastructural challenges serving predictions from the model. Now you can handle all of this in minutes using Cleanlab Studio.
For classifying product reviews, the deployed Cleanlab Studio model is more accurate than OpenAI LLMs fine-tuned on the same data. Producing this model merely required a few clicks in the platform which automatically: detect/correct issues in the dataset to produce a better version, identify and train the best ML model for this particular data, and deploy it for serving predictions in an application. Each of these steps typically requires significant code and effort from a team, but not if you use Cleanlab! Within hours, our cutting-edge AutoML with Foundation models produces highly accurate models for almost any dataset.
Cleanlab Studio allows you to rapidly turn raw image/text/tabular data into reliable ML model deployments, by automating all of the necessary steps. No other tool makes the full end-to-end pipeline this easy and performant!
Details on how we achieve this via novel Data-Centric AI techniques and benchmarks of model performance are in our new blogpost: http://cleanlab.ai/blog/model-deployment/
One problem not covered here is: knowing which data to review.
If your AI system produces say 95% accurate responses, your Evals team will spend too much time reviewing production logs to discover different AI failure modes.
To enable your Evals team to only spend time reviewing the high-signal responses that are likely incorrect, I built a tool that automatically surfaces the least trustworthy LLM responses:
https://help.cleanlab.ai/tlm/
Hope you find it useful, I made sure it works out-of-the-box with zero-configuration required!