This article explains the differences between Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and AI Agents, highlighting that they solve different problems at different layers of the AI stack. It also covers how ChatGPT routes prompts and handles modes, agent skills, architectural concepts for developers, and service deployment strategies.
A collection of prompts designed to be used with AI coding assistants to build various use cases, ranging from personal CRM and knowledge bases to content pipelines and social media research.
Here’s the simplest version — key sentence extraction:
<pre>
```
def extract_relevant_sentences(document, query, top_k=5):
sentences = document.split('.')
query_embedding = embed(query)
scored = »
for sentence in sentences:
similarity = cosine_sim(query_embedding, embed(sentence))
scored.append((sentence, similarity))
scored.sort(key=lambda x: x 1 » , reverse=True)
return '. '.join( s[0 » for s in scored :top_k » ])
```
</pre>
For each sentence, compute similarity to the query. Keep the top 5. Discard the rest
<pre>
```
def extract_relevant_sentences(document, query, top_k=5):
sentences = document.split('.')
query_embedding = embed(query)
scored = »
for sentence in sentences:
similarity = cosine_sim(query_embedding, embed(sentence))
scored.append((sentence, similarity))
scored.sort(key=lambda x: x 1 » , reverse=True)
return '. '.join( s[0 » for s in scored :top_k » ])
```
</pre>
For each sentence, compute similarity to the query. Keep the top 5. Discard the rest
The article discusses the evolution from RAG (Retrieval-Augmented Generation) to 'context engineering' in the field of AI, particularly with the rise of agents. It explores how companies like Contextual AI are building platforms to manage context for AI agents and highlights the shift from prompt engineering to managing the entire context state.
Logs, metrics, and traces aren't enough. AI apps require visibility into prompts and completions to track everything from security risks to hallucinations.
An AI-powered document search agent that explores files like a human would — scanning, reasoning, and following cross-references. Unlike traditional RAG systems that rely on pre-computed embeddings, this agent dynamically navigates documents to find answers.
FailSafe is an open-source, modular framework designed to automate the verification of textual claims. It employs a multi-stage pipeline that integrates Large Language Models (LLMs) with retrieval-augmented generation (RAG) techniques.
This article details how to build a 100% local MCP (Model Context Protocol) client using LlamaIndex, Ollama, and LightningAI. It provides a code walkthrough and explanation of the process, including setting up an SQLite MCP server and a locally served LLM.
An extensible Model Context Protocol (MCP) server that provides intelligent semantic code search for AI assistants. Built with local AI models using Matryoshka Representation Learning (MRL) for flexible embedding dimensions.
A curated repository of AI-powered applications and agentic systems showcasing practical use cases of Large Language Models (LLMs) from providers like Google, Anthropic, OpenAI, and self-hosted open-source models.
