RAG combines language models with external knowledge. This article explores context & retrieval in RAG, covering search methods (keywords, TF-IDF, embeddings/FAISS/Chroma), context length challenges (compression, re-ranking), and contextual retrieval (query & conversation history).
This article discusses how to effectively utilize Large Language Models (LLMs) by acknowledging their superior processing capabilities and adapting prompting techniques. It emphasizes the importance of brevity, directness, and providing relevant context (through RAG and MCP servers) to maximize LLM performance. The article also highlights the need to treat LLM responses as drafts and use Socratic prompting for refinement, while acknowledging their potential for "hallucinations." It suggests formatting output expectations (JSON, Markdown) and utilizing role-playing to guide the LLM towards desired results. Ultimately, the author argues that LLMs, while not inherently "smarter" in a human sense, possess vast knowledge and can be incredibly powerful tools when approached strategically.
Adafruit highlights the development of “pycoClaw,” a fully-featured AI agent implemented in MicroPython and running on a $5 ESP32-S3. This agent boasts capabilities like recursive tool calling, persistent memory using SD card storage, and a touchscreen UI, all built with an async architecture and optimized for performance through C user modules. The project is open-source and supports various hardware platforms, with ongoing development for RP2350, and is showcased alongside other Adafruit news including new product releases, community events, and resources for makers.
This article discusses how AI tools can be used to enhance the reading experience by providing instant access to information and background details, similar to using a dictionary or Wikipedia, but with the ability to ask more complex questions. The author shares personal examples of using AI while reading 'The Dark Forest' and other books to clarify plot points and gain a better understanding of the material.
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.
