Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and […]
Read more →Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
Supercharge Your Cloud Infrastructure with Amazon CDK v2: Python Power and Seamless Migration from CDK v1!
Imagine how efficient your cloud operations could be if you could use your familiar programming languages to define your cloud infrastructure? Interestingly, Amazon’s Cloud Development Kit (CDK) makes this possible. Developers can leverage high-level components to define their infrastructure in code, simplifying the process and giving them more control. This blog will delve into the […]
Read more →Chain-of-Thought Prompting: Unlocking LLM Reasoning with Step-by-Step Thinking
Introduction: Chain-of-thought (CoT) prompting dramatically improves LLM performance on complex reasoning tasks. Instead of asking for a direct answer, you prompt the model to show its reasoning step by step. This simple technique can boost accuracy on math problems from 17% to 78%, and similar gains appear across logical reasoning, code generation, and multi-step analysis. […]
Read more →Tool Use Patterns: Building LLM Agents That Can Take Action
Introduction: Tool use transforms LLMs from text generators into capable agents that can search the web, query databases, execute code, and interact with APIs. But implementing tool use well is tricky—models hallucinate tool calls, pass invalid arguments, and struggle with multi-step tool chains. The difference between a demo and production system lies in robust tool […]
Read more →Retrieval Augmented Generation Patterns: Building RAG Systems That Actually Work
Introduction: Retrieval Augmented Generation (RAG) grounds LLM responses in your actual data, reducing hallucinations and enabling knowledge that wasn’t in the training set. But naive RAG—embed documents, retrieve top-k, stuff into prompt—often disappoints. Retrieval misses relevant documents, context windows overflow, and the model ignores important information buried in long contexts. This guide covers advanced RAG […]
Read more →LLM Output Parsing: Extracting Structured Data from Free-Form Text
Introduction: LLMs generate text, but applications need structured data—JSON objects, lists, specific formats. The gap between free-form text and usable data structures is where output parsing comes in. Naive approaches using regex or string splitting break constantly as models vary their output format. Robust parsing requires multiple strategies: format instructions that guide the model, extraction […]
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