Towards Data Science
Aug 9
How to Implement Structured Output with Local LLMs
Why use it? How to implement it?
Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each
Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each appeared first on Towards Data Science .
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Towards Data Science
Jul 14
Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs
How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science .
OpenAI Blog
Aug 6, 2024
Introducing Structured Outputs in the API
We are introducing Structured Outputs in the API—model outputs now reliably adhere to developer-supplied JSON Schemas.
Towards Data Science
Aug 12
Building Multimodal Workflows with a Local LLM
Image inputs and structured outputs with Gemma 4 and Ollama The post Building Multimodal Workflows with a Local LLM appeared first on Towards Data Science .
Towards Data Science
Jun 14
Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG
Enterprise Document Intelligence [Vol. 1 #5quater] - The other parsers read the words on a page.
Towards Data Science
Jun 5
Automate Writing Your LLM Prompts
Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science .
Towards Data Science
Jun 27
How to Build a Powerful LLM Knowledge Base
Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science .
Towards Data Science
Jul 22
How To Build Your Own LLM Runtime From Scratch
If you have ever wanted to actually build an LLM inference runtime yourself — pack your own weights, own every barrier, capture your own CUDA graphs — this is what that journey looks like on an H100. A step-by-step tour of a small runtime called annotated-llm-runtime, and the three bugs that produced most of the annotations.
Towards Data Science
Jun 8
Increase Recommendation Systems’ Precision with LLMs, Using Python
This is how LLMs are used today to increase precision in recommendation systems The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science .
Towards Data Science
Jul 24
Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship
Enterprise Document Intelligence [Vol. 1 #8quater] - Two angles on the cascade, cost and a validation loop, backed by a real sweep of twenty local models against a hosted flagship The post Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship appeared first on Towards Data Science .
Towards Data Science
Aug 3
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
Enterprise Document Intelligence [Vol. 1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops).
Towards Data Science
Jun 24
Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .