Building Real-World LLM Products with Fine-Tuning and More with Hamel Husain - #694

Update: 2024-07-23

Digest

This podcast delves into the complexities of transitioning Gen AI proof of concepts into real-world applications, highlighting the critical role of Motific, an AI solution from Cisco's Outshift Incubation Engine, in addressing security, trust, compliance, and cost concerns. The episode then explores the strategic considerations behind fine-tuning LLMs, emphasizing the importance of narrow use cases, data privacy, and continuous evaluation. The podcast further underscores the crucial role of evaluation and measurement in building successful LLM applications, advocating for a process-driven approach that starts with basic assertions and gradually incorporates more sophisticated evaluation techniques. The discussion highlights the need for systematic testing and the development of domain-specific metrics to identify and address failure modes, ensuring the model's reliability and effectiveness in real-world scenarios.

Outlines

00:00:00

Bridging the Gap Between Gen AI Proof of Concept and Real-World Deployment

This episode discusses the challenges enterprises face in deploying Gen AI proof of concepts into real-world applications. It introduces Motific, an AI solution from Cisco's Outshift Incubation Engine, designed to accelerate LLM deployment by addressing security, trust, compliance, and cost concerns.

00:01:24

Fine-Tuning LLMs: When, Why, and How

The episode delves into the topic of fine-tuning LLMs, exploring when it's beneficial and how to approach it. The guest, Hamel Hussein, emphasizes that fine-tuning should be a strategic decision, not a default action. He highlights the importance of narrow use cases, data privacy, and the need for continuous evaluation.

00:55:42

The Importance of Evaluation and Measurement for LLMs

The podcast stresses the critical role of evaluation and measurement in building successful LLM applications. It emphasizes the need for systematic testing and the development of domain-specific metrics to identify and address failure modes. The guest advocates for a process-driven approach, starting with basic assertions and gradually incorporating more sophisticated evaluation techniques.

Keywords

Motific

Motific is an AI solution developed by Cisco's Outshift Incubation Engine. It aims to simplify and accelerate the deployment of large language models (LLMs) by addressing security, trust, compliance, and cost concerns. Motific is model and vendor-agnostic, making it compatible with various LLM platforms.

Fine-tuning

Fine-tuning is a technique used to adapt a pre-trained large language model (LLM) to a specific task or domain. It involves training the model on a dataset relevant to the target application, allowing it to perform better on that specific task. Fine-tuning can improve accuracy, efficiency, and data privacy.

eVals

eVals refers to the process of evaluating and measuring the performance of large language models (LLMs). It involves designing and implementing tests to identify and quantify failure modes, ensuring the model's reliability and effectiveness in real-world applications. eVals are crucial for building robust and trustworthy AI systems.

Prompt Engineering

Prompt engineering is the art of crafting effective prompts for large language models (LLMs) to elicit desired responses. It involves understanding the model's capabilities and limitations, designing prompts that guide the model towards the desired output, and iteratively refining prompts based on feedback.

Q&A

What are the key challenges enterprises face when deploying Gen AI proof of concepts?
Enterprises struggle with bridging the gap between Gen AI proof of concepts and real-world deployment due to concerns about security, trust, compliance, and cost.
When is fine-tuning LLMs a good strategy?
Fine-tuning is beneficial for narrow use cases, when data privacy is a concern, or when a smaller, more cost-effective model is desired.
Why is evaluation and measurement crucial for LLM applications?
Evaluation and measurement are essential for identifying and addressing failure modes, ensuring the model's reliability and effectiveness in real-world scenarios.
How can you approach evaluation and measurement for LLMs?
Start by writing assertions to identify and address simple failure modes. Gradually incorporate more sophisticated evaluation techniques, such as using an LLM as a judge, and ensure alignment between human and AI judgments.

Show Notes

Today, we're joined by Hamel Husain, founder of Parlance Labs, to discuss the ins and outs of building real-world products using large language models (LLMs). We kick things off discussing novel applications of LLMs and how to think about modern AI user experiences. We then dig into the key challenge faced by LLM developers—how to iterate from a snazzy demo or proof-of-concept to a working LLM-based application. We discuss the pros, cons, and role of fine-tuning LLMs and dig into when to use this technique. We cover the fine-tuning process, common pitfalls in evaluation—such as relying too heavily on generic tools and missing the nuances of specific use cases, open-source LLM fine-tuning tools like Axolotl, the use of LoRA adapters, and more. Hamel also shares insights on model optimization and inference frameworks and how developers should approach these tools. Finally, we dig into how to use systematic evaluation techniques to guide the improvement of your LLM application, the importance of data generation and curation, and the parallels to traditional software engineering practices.

The complete show notes for this episode can be found at https://twimlai.com/go/694.