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LLM

11 articles tagged with LLM.

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  1. The Mind's Eye - Engineering a CLI for Intelligent AI Interaction

    This article concludes the series by showing how a deliberately designed CLI becomes a powerful interaction layer, giving users precise control over an AI system's conversational context, short-term memory, and long-term semantic knowledge.

    AI Memory LLM

  2. The Autonomous Brain - Engineering AI for Continuous Learning and Memory Enrichment

    This piece introduces background processors as autonomous AI agents that summarise conversations and extract critical facts to continuously enrich Long-Term Semantic Memory. By running asynchronously and optimising token usage, these processors enable a self-improving, increasingly personalised AI system that learns from every interaction.

    AI Memory LLM

  3. The 'Aha!' Moment - Engineering the Perfect Prompt for Truly Contextual AI

    In this article, we show how dynamic prompt engineering—via a `SessionManager` that intelligently layers short-term context, long-term semantic memory, and system instructions turns stateless LLM calls into genuinely contextual and personalised conversations.

    AI Memory LLM

  4. Semantic Horizons - Engineering an AI's Enduring Long-Term Memory

    In this article, we explain how Long-Term Semantic Memory uses vector embeddings and semantic search to give AI meaningful, persistent memory across conversations.

    AI Memory LLM

  5. The Token Economy - Engineering an AI's Working Memory

    In this article, we explore how Short-Term Conversational Memory creates the illusion of memory in otherwise stateless LLMs through careful context persistence and structured prompt reconstruction. We also show how token limits, cost, and context degradation are managed using asynchronous, AI-driven summarisation that preserves meaning while keeping conversations efficient and coherent.

    AI Memory LLM

  6. The Grand Orchestration - Engineering a Dual-Memory AI for Enduring Conversations

    This article explains why LLMs often forget earlier messages and how naive full-history prompting is costly and inefficient. It introduces a dual-memory architecture: a short-term store for immediate conversation flow and a long-term semantic store for durable knowledge across sessions. Together, these systems let an AI maintain coherent dialogue without overloading the context window or budget.

    AI Memory LLM

  7. Detecting Hallucinations in Language Models with Natural Language Inference

    A practical look at why hallucinations occur in modern language models, why current evaluation methods make them persist, and how to detect them using Natural Language Inference in JavaScript.

    AI LLM NLP Transformers.js JavaScript

  8. The Importance of Precise Function Declarations for LLMs

    This article explores how clearly defined functions enable large language models to make accurate tool calls, emphasising the importance of precision and developer intent in the function calling process.

    AI LLM Function Calling

  9. Consuming Streamed LLM Responses on the Frontend: A Deep Dive into SSE and Fetch

    Learn how to build a responsive, real-time user experience by consuming streamed Large Language Model responses on your frontend. This article provides a comprehensive guide to using both Server-Sent Events (SSE) and the Fetch API with Readable Streams, complete with code examples and a detailed comparison.

    Web Development JavaScript LLM Frontend API

  10. Filling in the Blanks: Teaching AI to Inpaint

    A hands-on guide for exploring how to train a simple AI model using TensorFlow.js to inpaint missing parts of images - without needing large datasets or prior machine learning experience.

    LLM AI

  11. How Transformers and LLMs Actually Work - A Developer's Guide with Code

    A hands-on walkthrough for web developers to demystify large language models by actually building a mini Transformer from scratch.

    LLM AI