LLM Prompt Best Practices For Large Context Windows

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

This article delves into the nuances of using large language models (LLMs) with large context windows, highlighting the benefits and challenges they present, from enhancing coherence and relevance to demanding more computational resources. Learn practical strategies for prompt design, maintaining narrative coherence, and utilizing attention mechanisms effectively.

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Calculating Token Counts for LLM Context Windows: A Practical Guide

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

This article discusses the concept of token counts in large language models (LLMs) and their impact. Tokens are fragments of language used for text processing, representing words, parts of words, or punctuation marks. Code walkthroughs demonstrate how to calculate token counts and examples provide insight.

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The Problem of Big Data in Small Context Windows (Part 2)

Published
Author
Dr. Phil Winder
CEO

An introduction to the challenge of fitting big data into the context windows of LLMs. In this second installment, discover the key strategies involved to improve your use of the context window. Subsequent articles will provide more examples.

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ChatGPT from Scratch: How to Train an Enterprise AI Assistant

Published
Author
Dr. Phil Winder
CEO

This is a video of a presentation investigating how large language models are built and how to use them, inspired by our large language model consulting work. First presented at GOTO Copenhagen in 2023, the video investigates the history, the technology, and the use of large language models. The demo at the end is borderline cringe, but it’s a fun and demonstrates how you would fine-tune a language model on your proprietary data.

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Part 6: Useful ChatGPT Libraries: Productization and Hardening

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

LangChain and LlamaIndex streamline ChatGPT and LLM application development. Boost your project’s efficiency with LangChain’s tools and modules, and LlamaIndex’s advanced document handling. Discover the future of language model orchestration today.

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Part 5: How to Monitor a Large Language Model

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

The article explores the complexities and nuances of monitoring and evaluating Large Language Models (LLMs) like ChatGPT in business applications. It emphasizes the insufficiency of traditional metrics, the importance of real-time tracking, human feedback, and specialized evaluation methods to ensure model safety, efficiency, and performance optimization.

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Part 4: How to Deploy a ChatGPT Model or LLM

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

In our previous articles, you learned how to build and train your personal ChatGPT model (large-language model). However, it’s important to understand that these models are merely components within a larger software landscape. After achieving adequate performance in a controlled environment, the next step is to integrate it into your broader system.

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Part 3: Training Custom ChatGPT and Large Language Models

Published
Author
Natalia Kuzminykh
Associate Data Science Content Editor

In just a few years since the transformer architecture was first published, large language models (LLMs) have made huge strides in terms of performance, cost, and potential. In the previous two parts of this series, we’ve already explored the fundamental principles of such models and the intricacies of the development process.

Yet, before an AI product can reach its users, the developer must make yet more key decisions. Here, we’re going to dig into whether you should train your own ChatGPT model with custom data.

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