This article outlines advanced architectures and techniques for production and enterprise-scale AI systems, exploring cutting-edge model optimisation, sophisticated retrieval strategies, complex reasoning frameworks and robust security considerations that push the boundaries of what's possible with LLM and RAG implementations.
Building effective, production-ready LLM and RAG systems requires more than just theoretical knowledge. This intermediate guide outlines concepts and techniques for overcoming real-world implementation challenges, optimising performance, and ensuring system reliability. Whether you're scaling an existing deployment or planning your first production system, these essential insights will help you navigate the complexities of modern AI LLM & RAG architecture.
Here's a plain English explanation of key Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) concepts.
Sometime ago I started on an experiment combining stable diffusion with more traditional computer vision techniques. The goal? To create images that weren't just prompted by text, but guided by a starting image and also both visual style and content. Here are some the results and also a working implementation you can try yourself.
Speculative decoding makes large language models (LLMs) work more efficiently.
Large language models are transforming how we write code, but running them efficiently remains a challenge. Even with powerful hardware, code completion can feel sluggish, breaking our concentration just when we need it most. The bottleneck isn't necessarily computational power - it's how efficiently we use it. This is where speculative decoding comes in.
The biggest risk in AI projects isn't the technology - it's the gap between expectation and reality. But what if you could validate your AI solution in days, not months?
When marketing and advertising agencies develop AI-powered concepts for clients, they face a practical challenge: how to validate technical feasibility before committing significant resources. Traditional approaches involving detailed specifications and lengthy proposals often prove inefficient with AI projects, where real-world performance can differ significantly from paper specifications.
This is a demonstration of what is possible with rapid prototyping and iterative refinement using AI dialogue engineering tools. This is a prototype of context-aware image locally running search engine that combines CLIP content relevance and Stable Diffusion style embeddings. This type of search could be useful for anyone with large collections of images that are difficult to search, online shops selling stock images or cards, museums or cases where images can't be put into cloud searches due to being business critical or classified.
It's 3 AM, and you're wide awake. Your company just deployed a ChatGPT-powered customer service bot, and your mind is racing with questions: "What if it starts giving incorrect information? What if it speaks inappropriately to customers? What if it leaks sensitive data?".
This started off as a bit of fun. I worked with an AI Assistant using dialogue engineering and iterative refinement to create a receipe for very tasty roast potatoes prepared a day in advance, so that I can just reheat them. The family thought these were the most tasty roast potatoes they had ever had. Note these where much better than my ChatGPT roast potatoes I tried a couple of months ago.
DeepSeek, an AI research organization, has recently released a new open-source AI model that's generating interest in the tech community. It presents some interesting possibilities for businesses considering AI implementation.