📝 Guest Post: Build Trustworthy LLM Apps With Rapid Evaluation, Experimentation and Observability*
📝 Guest Post: Build Trustworthy LLM Apps With Rapid Evaluation, Experimentation and Observability*
In this guest post, Vikram Chatterji, CEO and co-founder of Galileo, introduces us to their LLM Studio. It provides a powerful evaluation, experimentation and observability platform across the LLM application development lifecycle (prompting with RAG, fine-tuning, production monitoring) to detect and minimize hallucinations through a suite of evaluation metrics. You can learn more about Galileo LLM Studio through their webinar on Oct 4. Let’s dive in.
With large language models (LLMs) increasing in size and popularity we, as a data science community, have seen new needs emerge. LLM-powered apps have a different development lifecycle than traditional NLP-powered apps – prompt experimentation, testing multiple LLM APIs, RAG and LLM fine-tuning. In speaking with practitioners across financial services, healthcare, and AI-native companies, it has become clear that LLMs require a new development toolchain.
Namely, three big challenges facing LLM developers stand out:
The need for holistic evaluation – Traditional NLP metrics no longer apply, making manual, painstaking, and error-prone human analysis the norm.
The need for rapid experimentation – Making LLMs production-ready requires trying a dozen variations of prompts, LLMs, and parameters. Managing hundreds of permutations in notebooks or sheets makes experimentation ineffective and untenable.
The need for actionable observability – When models meet the world, constant attention is mandatory. LLMs hallucinate and the need to monitor this unwanted behavior via scientific metrics and guardrails is critical.
Introducing Galileo LLM Studio
LLM Studio helps you develop and evaluate LLM apps in hours instead of days. It is designed to help teams across the application development lifecycle, from evaluation and experimentation during development to observability and monitoring once in production.
LLM Studio offers three modules - Prompt, Fine-Tune, and Monitor - so whether you’re using RAG or fine-tuning, LLM Studio has you covered.
1. Prompt
Prompt engineering is all about experimentation and root-cause analysis. Teams need a way to experiment with multiple LLMs, their parameters, prompt templates, and context from vector databases.
The Prompt module helps you systematically experiment with prompts in order to find the best combination of prompt template, model, and parameters for your generative AI application. We know prompting is a team sport, so we’ve built features to enable collaboration with automatic version controls. Teams can use Galileo’s powerful suite of evaluation metrics to evaluate outcomes and detect hallucinations.
2. Fine-Tune
When fine-tuning an LLM, it is critical to leverage high-quality data. However, data debugging is painstaking, manual, and needs a bunch of iterations – moreover, leveraging labeling tools here balloons cost and time.
The Fine-Tune module is an industry-first product built to automatically identify the most problematic training data for the LLM – incorrect ground truth, regions of low data coverage, low-quality data, and more. Coupled with collaborative experiment tracking and 1-click similarity search, Fine-Tune is perfect for data science teams and subject matter experts to work together towards building high-quality custom LLMs.
3. Monitor
Prompt engineering and fine-tuning are half the journey. Once an application is in production and in end-customers’ hands, the real work begins. Generative AI builders need governance frameworks in place to minimize the risk of LLM hallucinations in a scalable and efficient manner. This is especially important as generative AI is still in the early innings of winning end-user trust.
To help with this, we’ve built Monitor, an all-new module that gives teams a common set of observability tools and evaluation metrics for real-time production monitoring. Apart from the usual tracing available, Monitor ties application metrics like user engagement, cost, and latency to ML metrics used to evaluate models and prompts during training, like Uncertainty, Factuality, and Groundedness. Teams can set up alerts so they can be notified and conduct root-cause analysis the second something seems off.
A Unified Platform to Drive Continuous Improvement
While each of these modules provides value in its own right, the greatest value-unlock comes from these modules operating on a single fully integrated platform.
A core principle for building AI powered apps should be ‘Evaluation First’ - everything starts and ends with the ability to evaluate and inspect your application.
This is why Galileo offers a Guardrail Metrics Store - equipped with a common set of research-backed evaluation metrics that users can use across Prompt, Fine-Tune, and Monitor.
Our Guardrail Metrics include powerful new metrics from Galileo's in-house ML Research Team (e.g. Uncertainty, Factuality, Groundedness). You can also define your own custom evaluation metrics.
Together, these metrics help teams minimize the risk of LLM hallucinations and bring more trustworthy applications to market.
Building LLM powered applications can be tricky. Poor quality prompts, context, data or LLMs can quickly lead to hallucinatory responses.
To find out more about how you can perform metrics-powered evaluation and experimentation across the LLM app development lifecycle, sign up for our upcoming webinar here!