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157 matches · Showing 1–18
Courses and guides are learning content. Directories organise choices; topic roadmaps and guidance requests do not imply a scheduled class or a confirmed mentor.
- GuideFree guide · read nowBuild your first LLM app: a practical path for developersFrom API call to a small, tested feature: the steps, decisions, and checks for a first production-minded LLM application.View details
- GuideFree guide · read nowRetrieval-augmented generation (RAG) explained with a working mental modelHow RAG works, where it fails, and the design decisions — chunking, retrieval, prompting, evaluation — that decide quality.View details
- GuideFree guide · read nowHow to evaluate LLM outputs: test sets, rubrics, and automated checksBuild evaluation into LLM development with representative test sets, clear rubrics, automated scoring, and human review where it counts.View details
- GuideFree guide · read nowPrompt injection: what it is and how to defend LLM applicationsUnderstand direct and indirect prompt injection, why it's hard to fully prevent, and layered defences for real applications.View details
- GuideFree guide · read nowAI agents for developers: tools, loops, and guardrailsWhat makes an LLM application an agent, how tool calling works, and the guardrails agents need before they touch real systems.View details
- GuideFree guide · read nowGetting reliable JSON and structured output from LLMsTechniques for dependable structured output: schemas, provider features, validation, retries, and graceful failure.View details
- GuideFree guide · read nowAI pair programming: habits that make coding assistants actually helpHow to get useful code from AI assistants — context, small steps, tests, and review — without shipping bugs or security holes.View details
- GuideFree guide · read nowReducing LLM costs without hurting qualityPractical levers — model choice, prompt size, caching, batching, and routing — to control LLM spend, measured against your evaluation set.View details
- GuideFree guide · read nowPython for AI: what to learn first (and what to skip for now)A focused Python learning path for people heading into data or AI work — core language, data libraries, APIs — without drowning in theory.View details
- GuideFree guide · read nowProduction checklist for shipping an AI featureA pre-launch checklist covering evaluation, safety, privacy, observability, cost, and fallbacks for LLM-powered features.View details
- GuideFree guide · read nowPrompting, RAG, or fine-tuning? Choosing the right approachA decision guide for when to improve prompts, add retrieval, or fine-tune a model — based on the problem you're actually solving.View details
- GuideFree guide · read nowUsing AI for software testing: test ideas, test code, and dataGenerate test cases, write test code, create realistic test data, and explore edge cases with AI — while keeping tests meaningful.View details
- GuideFree guide · read nowBuilding accessible AI chat and assistant interfacesAccessibility essentials for AI interfaces: streaming text, focus management, screen reader announcements, and clear controls.View details
- GuideFree guide · read nowOpen-weight LLMs: when to self-host and what it really takesTrade-offs between hosted APIs and self-hosted open-weight models: control, privacy, cost, quality, and operational load.View details
- GuideFree guide · read nowModel Context Protocol (MCP): a practical introductionWhat MCP is, how clients and servers fit together, and what to consider before connecting AI assistants to your tools and data.View details
- GuideFree guide · read nowAI projects for students that stand out in a portfolioProject ideas at three levels — beginner, intermediate, advanced — with what makes each credible to recruiters.View details
- GuideFree guide · read nowHow to use AI for studying without cheating yourselfUse AI as a tutor — explanations, practice questions, feedback — while following your institution's rules and actually learning.View details
- GuideFree guide · read nowGetting an internship with AI skills: a step-by-step planBuild a focused skill set, a project that fits the internship, and an application that shows evidence — with India-specific places to look.View details