LLM applications

Context Engineering — A Comprehensive Hands-On Tutorial with DSPy

Context Engineering by now. This text will cover the important thing ideas behind creating LLM applications using Context Engineering principles, visually explain these workflows, and share code snippets that apply these concepts practically. Don’t...

Learn how to Ensure Reliability in LLM Applications

have entered the world of computer science at a record pace. LLMs are powerful models able to effectively performing a wide selection of tasks. Nonetheless, LLM outputs are stochastic, making them unreliable. In...

Use OpenAI Whisper for Automated Transcriptions

development currently with large language models (LLMs). A number of the main focus is on the question-answering you may do with each pure text-based models, or vision-language models (VLMs), where you may also...

Data Has No Moat!

of AI and data-driven projects, the importance of information and its quality have been recognized as critical to a project’s success. Some might even say that projects used to have a single point...

Construct and Query Knowledge Graphs with LLMs

Graphs are relevant A Knowledge Graph could possibly be defined as a structured representation of knowledge that connects concepts, entities, and their relationships in a way that mimics human understanding. It is commonly used to...

AI Struggles to Emulate Historical Language

A collaboration between researchers in the USA and Canada has found that giant language models (LLMs) comparable to ChatGPT struggle to breed historical idioms without extensive pretraining – a costly and labor-intensive process that...

A Step-By-Step Guide To Powering Your Application With LLMs

whether GenAI is just hype or external noise. I also thought this was hype, and I could sit this one out until the dust cleared. Oh, boy, was I flawed. GenAI has real-world...

Retrieval Augmented Generation (RAG) — An Introduction

. We’ve all heard or experienced it. Natural Language Generation models can sometimes hallucinate, i.e., they begin generating text that just isn't quite accurate for the prompt provided. In layman’s terms, they begin ...

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