We are experimenting with livestreams! We try to explore new tools and techniques weekly. This week we've invited William Bakst to talk about mirascope, a mo...

LLM abstractions that aren't obstructions. Contribute to f8n-ai/mirascope development by creating an account on GitHub.

We are experimenting with livestreams! We try to explore new tools and techniques weekly. This week we've invited William Bakst to talk about mirascope, a mo...

A poem by Brian Orozco: “After the step I assumed / was last was another.”

Amazon.com: TOYANDONA Illusion Image Projection Toy, 3D Mirascope Instant Holographic Mirror Visual Parabolic Optical Hologram Maker Children Educational Toy Reflector Frog Model Kids Boys Girls : Toy...

About This Episode We watch a new comedy thriller series about an unlikely workpla

This tutorial demonstrates find out how to implement the Self-Refine approach utilizing Giant Language Fashions (LLMs) with Mirascope, a robust framework for constructing structured immediate workflows. Self-Refine is a immediate engineering technique the place the mannequin evaluates its personal output, generates suggestions, and iteratively improves its response primarily based on that suggestions. This refinement loop may be repeated a number of occasions to progressively improve the standard and accuracy of the ultimate reply. The Self-Refine strategy is especially efficient for duties involving reasoning, code technology, and content material creation, the place incremental enhancements result in considerably higher outcomes.

This tutorial demonstrates how to implement the Self-Refine technique using Large Language Models (LLMs) with Mirascope, a powerful framework for building structured prompt workflows. Self-Refine is a prompt engineering strategy where the model evaluates its own output, generates feedback, and iteratively improves its response based on that feedback. This refinement loop can be repeated multiple times to progressively enhance the quality and accuracy of the final answer.

On this tutorial, we’ll present the way to create a Information Graph from an unstructured doc utilizing an LLM. Whereas conventional NLP strategies have been used for extracting entities and relationships, Giant Language Fashions (LLMs) like GPT-4o-mini make this course of extra correct and context-aware. LLMs are particularly helpful when working with messy, unstructured information. Utilizing Python, Mirascope, and OpenAI’s GPT-4o-mini, we’ll construct a easy data graph from a pattern medical log. Putting in the dependencies !pip set up "mirascope" matplotlib networkx OpenAI API Key To get an OpenAI API key, go to and generate a brand new key.

In this tutorial, we’ll show how to create a Knowledge Graph from an unstructured document using an LLM. While traditional NLP methods have been used for extracting entities and relationships, Large Language Models (LLMs) like GPT-4o-mini make this process more accurate and context-aware. LLMs are especially useful when working with messy, unstructured data. Using Python, Mirascope, and OpenAI’s GPT-4o-mini, we’ll build a simple knowledge graph from a sample medical log.

A poem by Brian Orozco: “After the step I assumed / was last was another.”