Showing posts from Llm category
Vector Databases Explained: Choosing the Right One for Your AI Application
Vector databases are now a core piece of production AI infrastructure. Whether you're building a RAG...
Fine-Tuning Llama 3 with QLoRA: A Practical Guide
QLoRA (Quantized Low-Rank Adaptation) changed the economics of LLM fine-tuning. Before QLoRA, fine-t...
How CTOs Should Build an LLM Evaluation Framework
The evaluation decision CTOs need to make An LLM evaluation framework converts "does this feel bette...
The Complete Guide to LLM Evaluation in Production
Evaluating LLMs is one of the least glamorous parts of AI engineering — and one of the most importan...
Prompt Engineering at Scale: Moving Beyond Hacks
Prompt engineering has a reputation problem. For many developers, it conjures images of trial-and-er...
How to Make a RAG System Reliable in Production
The reliability boundary in RAG A reliable RAG system retrieves authorized, current, relevant eviden...
RAG vs Fine-Tuning: How to Choose the Right Approach for Your LLM
One of the most common questions we get from engineering teams is: "Should we fine-tune our model or...
Reducing LLM Inference Costs by 60%: A Case Study
When a B2B SaaS company came to us, their AI features were a success story — too successful. Their m...