AI Engineer Portal
Your personal operating system for career transition.
LLM App Foundations
Learn the product and systems primitives behind useful LLM applications.
Prompt, context, tools, and memory
Map the core components of an LLM application before choosing frameworks.
Prompt, context, tools, and memory
Map the core components of an LLM application before choosing frameworks.
Request lifecycle of an LLM feature
Trace a user request from UI input to final response and logging.
Guardrails and structured outputs
Constrain outputs with schemas, system prompts, and approval boundaries.
Cost, latency, and failure budgets
Make tradeoffs visible instead of treating providers like magical black boxes.
Portfolio slice: ship one narrow assistant well
Design a focused feature instead of a vague chatbot.
Provider SDK integration patterns: OpenAI and Anthropic
Wire up production-grade clients for both major providers, handle auth, streaming, and error normalization, and build a provider-agnostic wrapper that survives API changes.
Prompt templating and multi-turn conversation management
Build reusable prompt templates with variable injection, manage multi-turn conversation state with proper history trimming, and implement the summarize-on-overflow pattern for long sessions.
Structured outputs, JSON mode, and response validation
Get reliably structured data out of LLMs using tool use, JSON mode, and Pydantic validation; implement defense-in-depth parsing for production features; handle model fallback chains for resilience.
LangChain fundamentals: chains, prompts, and parsers
Learn when LangChain adds value, how LCEL composes pipelines, and how to produce structured output with Pydantic parsers.
Provider abstraction patterns
Compare LangChain's model interface against direct SDK calls, understand the callback system, and decide when abstraction pays off.
Fine-tuning vs RAG: decision framework
A systematic decision framework for choosing between fine-tuning and RAG, covering dataset curation, LoRA/QLoRA, hybrid approaches, and the specific criteria that make each technique the right tool for the job.
Multi-modal AI for application engineers
Practical patterns for vision, audio, and document processing in production AI applications: sending images to vision models, speech-to-text and TTS integration, PDF and OCR pipelines, and building multi-modal chains.