Validating Low-Confidence LLM Generation

The basic approach has a hallucination detection phase and a mitigation phase.

Random Chain-Of-Thought For LLMs & Distilling Self-Evaluation Capability

Here I discuss the five emerging architectural principles for LLM implementations & how curation & enrichment of...

Prompt Pipelines

LLM-based applications can take the form of autonomous agents, prompt chaining or prompt pipelines. These approaches...

Active Prompting with Chain-of-Thought for Large Language Models

By using AI accelerated human annotation this framework removes uncertainty and introduces reliability via a...

Teaching LLMs To Say, “I don’t know”

Instead of stating that it does not know, LLMs hallucinate. Hallucination can best be described as highly plausible,...

Improving Text Embeddings with LLM Generated Synthetic Data

Value Discovered

Text embeddings are really playing a pivotal role in retrieving semantically similar text for RAG...

LLM Performance Over Time & LLM Task Contamination

A recent study revealed that LLMs perform significantly better on datasets released before their training data creation...

Data Design For Fine-Tuning LLM Long Context Windows

Fine-Tune your LLM To fully utilise the available context window.

Something I find intriguing considering recent...

Five Levels Of AI Agents

This is a topic I really enjoyed researching and I was looking forward to writing this. Mostly because I wanted to...

Comparing LLM Agents to Chains: Differences, Advantages & Disadvantages

In this article I put into plain terms the difference between chains and agents, and what will work best for certain...