Building LM Applications: A Step-by-Step Guide

🔨Start by building a retrieval workflow and retrieving relevant context for the given query.

📚Use the retrieved context and feed it into the LM to generate accurate and informative responses.

🧩Experiment and fine-tune the retrieval process and LM generation to improve the overall application performance.

💡Consider the limitations of LM context window and explore techniques like chunking and combining multiple embeddings for longer documents.

🔬Evaluate the quality of LM responses using established metrics and compare different LM models for optimal performance.

What is the first step in building LM applications?

—The first step is to build a retrieval workflow to retrieve relevant context for the given query.

How can I improve the accuracy of LM responses?

—To improve accuracy, provide the retrieved context to the LM for generating responses.

What are some techniques to handle longer documents in LM applications?

—Techniques like chunking and combining multiple embeddings can be used to handle longer documents.

How can I evaluate the quality of LM responses?

—You can evaluate the quality of LM responses using established metrics and compare different LM models for optimal performance.

What are the key insights for building LM applications?

—The key insights include building a retrieval workflow, using retrieved context in LM generation, fine-tuning the process, considering limitations, and evaluating performance.

00:05Start by building a retrieval workflow and retrieving relevant context for the given query.

02:21Use the retrieved context and feed it into the LM to generate accurate and informative responses.

04:48Experiment and fine-tune the retrieval process and LM generation to improve the overall application performance.

08:45Consider the limitations of LM context window and explore techniques like chunking and combining multiple embeddings for longer documents.

11:14Evaluate the quality of LM responses using established metrics and compare different LM models for optimal performance.