TechStudio Originals
AI Engineering & Career Guides
Original practical guides designed to help learners understand AI engineering concepts, make implementation decisions, build projects, and prepare for technical roles.
RAG Chunking Strategies: A Practical Guide
Fixed, recursive, semantic, and structure-aware chunking with an evaluation workflow.
Read guide →GuideHybrid Search RAG: BM25 + Vector Search + Reranking
How lexical retrieval, semantic retrieval, and reranking work together in production RAG.
Read guide →GuideProduction AI Agent Architecture
A practical architecture for tool use, state, permissions, retries, and observability.
Read guide →GuideMCP Explained for AI Engineers
Understand the Model Context Protocol, servers, tools, resources, and practical integration patterns.
Read guide →GuideLLM Evaluation: A Practical Framework
How to build evaluation datasets, retrieval metrics, answer-quality checks, and regression tests.
Read guide →GuideVector Databases: Concepts and Production Decisions
Embeddings, indexing, metadata filtering, similarity search, and operational trade-offs.
Read guide →GuideHow to Design a Production AI Project
A repeatable approach for turning an AI prototype into a maintainable application.
Read guide →GuideAI Engineer Roadmap: From Fundamentals to Production
A practical learning sequence from Python and ML fundamentals to RAG, agents, evaluation, and deployment.
Read guide →GuideAI Engineer Resume Guide
How to present AI projects, technical skills, impact, and evidence clearly for technical roles.
Read guide →New Deep-Dive Guides
AI Engineering & Career Library
Practical guides focused on implementation, evaluation, security, backend engineering, portfolios, and career preparation.
Hybrid Search RAG: BM25 + Vector Search + Reranking
A practical guide to combining lexical and semantic retrieval, reranking candidates, measuring retrieval quality, and designing a reliable search layer.
Read guide →AI EngineeringLLM Evaluation: A Practical Framework for AI Applications
How to evaluate AI systems beyond a single “accuracy” number using task metrics, groundedness, retrieval quality, latency, cost, and human review.
Read guide →AI AgentsProduction AI Agents: Architecture, Tools, Memory and Guardrails
A practical architecture for agents that call tools, maintain state, recover from failures, and operate within explicit safety and cost boundaries.
Read guide →MCPMCP for AI Engineers: Concepts, Servers, Tools and Design Patterns
A practical introduction to Model Context Protocol and how to design reliable tool interfaces for AI applications.
Read guide →RAGVector Databases Explained: Indexes, Metadata and Retrieval Trade-offs
Understand embeddings, approximate nearest-neighbor indexes, metadata filtering, hybrid retrieval, and the trade-offs behind vector database choices.
Read guide →BackendBuilding an AI API with FastAPI: From Prototype to Production
A practical blueprint for serving an LLM or RAG application through a clean API with validation, streaming, error handling, and observability.
Read guide →AI SecuritySecuring RAG Applications: Prompt Injection, Data Access and Leakage
A defensive guide to protecting retrieval-augmented applications against malicious instructions, unauthorized retrieval, and sensitive-data leakage.
Read guide →LLMProduction Prompt Engineering: Templates, Variables, Structured Outputs and Tests
Move prompt engineering from ad-hoc strings to versioned, testable application components with clear contracts and regression checks.
Read guide →CareerHow to Build an AI Engineering Portfolio That Demonstrates Real Skills
A practical framework for choosing, implementing, documenting, and presenting AI projects so that technical reviewers can understand the engineering decisions.
Read guide →CareerAI Engineer Resume Guide: Skills, Projects, Impact and ATS-Friendly Structure
A practical guide to presenting AI engineering experience without turning a resume into a keyword dump.
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