Posted inAI
Mastering Phidata: Building AI Assistants with ‘Memory’ and Task Execution Capabilities
Learn how to build AI Assistants with memory and automated task execution using Phidata. A lightweight alternative to LangChain.
Posted inAI
GPTCache: The Secret to Reducing OpenAI Bills by 80% and Speeding Up AI App Responses
Are your OpenAI API bills too high? Learn how to configure GPTCache to reduce costs by 80% and slash AI response times to milliseconds using Semantic Caching.
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Extracting Data from LLMs: Stop Manual JSON Parsing, Use Instructor Instead
Stop worrying about JSON parsing errors with LLMs. Learn how to use Instructor to extract structured data accurately, reliably, and cost-effectively for Python projects.
Posted inAI
Mastering NeMo Guardrails: Controlling Content and Eliminating LLM Hallucinations
NeMo Guardrails is a leading solution for controlling LLM content, preventing prompt injection, and eliminating hallucinations in real-world AI applications.
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Optimizing AI Systems with DSPy: It’s Time to Stop Manual Prompt Engineering
Stop wasting time on manual prompt engineering. Learn how DSPy lets you program LLM logic and automatically boost performance from 65% to 88% using real data.
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Langfuse (Self-hosted) Guide: Monitoring and Optimizing LLM Costs from A-Z
A detailed guide on deploying Self-hosted Langfuse with Docker for LLM monitoring. Practical tips on token management, prompt debugging, and cutting API costs by 30%.
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Sub-agents in Claude Code: Delegating Work to Specialized Agents
Sub-agents in Claude Code let you break complex tasks into multiple specialized agents running in parallel, each with its own isolated context. This guide covers how to configure them, pass context between agents, and monitor output to ensure accuracy.
Posted inAI
Evaluating RAG with RAGAS: From ‘Gut Feeling’ to Faithfulness, Answer Relevance, and Context Precision Metrics
Stop evaluating AI chatbots based on gut feeling. Use RAGAS to accurately measure Faithfulness, Answer Relevance, and Context Precision.
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Cleaning PDF/Office Data for RAG with Unstructured.io: Turning ‘Trash’ into Gold
Discover how Unstructured.io solves data extraction from PDF and Office files for RAG. Boost accuracy by 35% with smart table processing and text cleaning.
Posted inAI
Building AI Agents with LangGraph: Solving Complex Workflows and Self-Correction
A detailed guide on building advanced AI Agents using LangGraph and Python. This article focuses on state management, loop handling, and practical experience in optimizing API costs.