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 strathweb.com
Tool Calling with Azure OpenAI - Part 1: The Basics | Strathweb. A free flowing tech monologue.
| | ollama.com
Streaming responses with tool calling ยท Ollama Blog
3.7 parsecs away

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| | Ollama now supports streaming responses with tool calling. This enables all chat applications to stream content and also call tools in real time.
| | www.jamesserra.com
Introduction to OpenAI and LLMs | James Serra's Blog
3.6 parsecs away

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| | [AI summary] A technical blog post explains the differences and use cases for RAG versus fine-tuning when applying OpenAI Large Language Models to unstructured company data.
| | isthisit.nz
Prompting GPT Models to Return JSON
2.6 parsecs away

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| | August 2024 Update: Now a solved problem. Use Structured Outputs. Large language models (LLMs) return unstructured output. When we prompt them they respond with one large string. This is fine for applications such as ChatGPT, but in others where we want the LLM to return structured data such as lists or key value pairs, a parseable response is needed. In Building A ChatGPT-enhanced Python REPL I used a technique to prompt the LLM to return output in a text format I could parse.
| | www.e4developer.com
Microservices Toolbox: Spring Boot | E4developer
20.6 parsecs away

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| High level introduction to Spring Boot and its popularity. Spring Boot Hello World example and a list of features that make it so successful.