Microsoft’s POML Explained With a Simple Example! (+Ollama)

Quick Overview

Microsoft's Prompt Orchestration Markup Language (POML) standardizes prompts for large language models (LLMs) using an HTML-like syntax, enabling consistent and accurate results, as demonstrated by integrating with Ollama to answer customer order questions using CSV data and instructions.

Key Points: Microsoft's POML is an open-source Prompt Orchestration Markup Language that standardizes prompts for LLMs, similar to how HTML standardizes web pages. POML uses an HTML-like tag structure to define roles, tasks, data sources (including CSV and JSON), and output formats, leading to consistent results. The tutorial demonstrates integrating POML with Ollama by creating a 'qa.poml' file for customer order queries using CSV data and a 'app.py' script to load and execute the prompt. Running the application with Ollama and the 'qa.poml' file correctly answered the question "how much did I pay for my last order?", returning that the latest order (183) had a total of $0. POML can handle complex prompts with multiple data types, including multimedia like images, as shown in an advanced example using a financial analysis prompt with images and the 'Qwen 2.5 vision language model'. The system automatically handles data embedding, such as base64 conversion for images, simplifying the process of passing rich data to LLMs.

Context: This video introduces and demonstrates Microsoft's Prompt Orchestration Markup Language (POML), an open-source framework designed to standardize prompts for Large Language Models (LLMs). It explains how POML, using an HTML-like syntax, helps overcome the inconsistencies of unstructured text prompts by allowing structured inclusion of tasks, data sources (like CSV files or images), and desired output formats. The tutorial focuses on integrating POML with Ollama, a local LLM execution tool, to show how developers can achieve more reliable and accurate responses from AI models.

Detailed Analysis

The video explains Microsoft's Prompt Orchestration Markup Language (POML), an open-source tool akin to HTML, designed to standardize prompts for Large Language Models (LLMs) to ensure consistent and accurate outputs. Traditional prompts, often unstructured text, lead to variability and errors. POML uses structured tags for tasks, data sources (like CSVs, images), and output formats, creating a standardized prompt that can be passed to LLMs. The tutorial demonstrates integrating POML with Ollama, a model provider, to achieve more accurate answers. The process involves setting up Ollama by downloading and pulling a model like 'GPT4ALL', creating two files: 'qa.poml' for the POML code and 'app.py' for the Python application. The 'qa.poml' file defines roles, tasks, and incorporates data from CSV files ('orders.csv', 'orderline.csv') and JSON instructions, including looping through data. A Visual Studio Code extension for POML is recommended for previewing the rendered output, which includes embedded data. The 'app.py' script then loads and renders the POML file, combines messages, and calls the Ollama endpoint with the specified model ('GPT4ALL' or 'LLaMA2'). The demonstration shows how POML automatically handles data inclusion and even base64 conversion for multimedia, facilitating complex prompts with multiple data types, such as financial analysis with image data, by using models like 'Llama2:13b-chat-q4KM' or vision models like 'qwen:1.8b-chat-v1.5-q4KM'. The latter example with images shows a basic response due to the model's limitations, emphasizing that better models yield better results. The presenter also mentions Microsoft's open-source agentic system as a related topic.