Why Output Tokens Cost More Than Input Tokens — A Hardware Drill-Down
Every AI provider charges you separately for input and output tokens, and output is the expensive half — four to eight times the input rate (Anthropic prices …
AI
MCP + RAG: Semantic Code Search Over Your GitHub Repos
This blog post explains the RAG (retrieval-augmented generation) pipeline behind semcode, an MCP server that does semantic code search across your GitHub …
LLM Parameters: Temperature & Top-P
When working with Large Language Models via API, understanding the temperature and top-p parameters is essential for transforming basic text generation into …
Contextual Retrieval
In previous posts, we have discussed the RAG systems and how they work. We have also discussed the problem of lost context in embedded documents.
RAG Reranking
RAG re-ranking is the process of re-ordering retrieved documents using a more accurate relevance model so the LLM sees the best possible context. Let’s break it …
Embeddings
Definition: Embeddings are vectors that represent semantic meaning in a multi-dimensional space.
RAG (Retrieval Augmented Generation)
RAG (Retrieval-Augmented Generation) is a technique that addresses the limitation of LLMs not having domain-specific or up-to-date knowledge by augmenting the …