Embeddings Explained: How AI Turns Text into Vectors
A practical explanation of embeddings, similarity, vector dimensions, chunking and how embeddings power search, RAG and recommendations.
AI category
Applied intelligence
LLM systems, retrieval, evaluation and the workflows that make models useful instead of impressive.
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- 8GBVRAM local LLM
- 3xretrieval accuracy
- 0hype tolerated
All AI articles
11 in-depth AI guides, newest first.
Vector Databases Explained: Indexes, Metadata and Retrieval
Understand vector databases, approximate nearest-neighbour indexes, metadata filtering and the design decisions behind production retrieval systems.
AI Agents vs RAG: What Is the Real Difference?
A practical comparison of AI agents and RAG: retrieval, tools, planning, memory, failure modes, costs and when to combine both.
LLM Inference Optimization: Latency, Throughput and Memory
Learn the core levers for faster LLM inference: quantization, batching, KV cache, context length, speculative decoding and model choice.
Prompt Engineering for Production AI Systems
Move beyond clever prompts with production patterns for instructions, structured outputs, examples, failure handling, evaluation and prompt versioning.
Technical SEO for AI Websites: Crawlability, Canonicals and Sitemaps
A developer-focused technical SEO checklist for AI and tech sites covering crawlability, canonical URLs, robots.txt, sitemaps, internal links and indexing diagnostics.
Internal Linking for Topical Authority
A practical internal-linking framework for building topical authority: pillar pages, supporting articles, anchor text, click depth and link maintenance.
30 ChatGPT Prompts That Save an Hour a Day
30 ChatGPT prompts I actually use for email, meetings, spreadsheets and writing - copy, paste, replace the brackets. Plus the three prompts I stopped using.
RAG vs Fine-Tuning: How to Choose
RAG vs fine-tuning explained with a clear decision framework: cost, freshness, accuracy, latency and when to combine both approaches.
AI SEO: Building Topical Authority That Ranks #1
How to build topical authority with AI-assisted workflows: entity coverage, internal linking clusters, structured data and content that survives AI search.
Run a Local LLM on 8GB VRAM: Practical Setup
A practical guide to running a local LLM on 8GB VRAM: model choices, quantisation levels, context limits, throughput expectations and tuning tips.
Latest updates in AI
Maximum four newest AI posts, generated automatically.
Embeddings Explained: How AI Turns Text into Vectors
A practical explanation of embeddings, similarity, vector dimensions, chunking and how embeddings power search, RAG and recommendations.
Vector Databases Explained: Indexes, Metadata and Retrieval
Understand vector databases, approximate nearest-neighbour indexes, metadata filtering and the design decisions behind production retrieval systems.
AI Agents vs RAG: What Is the Real Difference?
A practical comparison of AI agents and RAG: retrieval, tools, planning, memory, failure modes, costs and when to combine both.
LLM Inference Optimization: Latency, Throughput and Memory
Learn the core levers for faster LLM inference: quantization, batching, KV cache, context length, speculative decoding and model choice.
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