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Searchable notes, comparisons, and architecture patterns.
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architecture
A clean handoff between ingestion and retrieval
The indexing layer should emit chunks and metadata in a form the retrieval layer can trust without guesswork.
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A decision guide for prompting, RAG, and fine-tuning
Pick the right technique based on knowledge freshness, control needs, and operational cost.
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A portfolio roadmap for the AI engineer transition
Pair learning paths with portfolio artifacts so each month produces visible proof, not just study notes.
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A practical schema for AI request traces
Store enough detail to debug context, prompts, outputs, latency, and cost without overwhelming yourself.
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AI system design interviews
A practical note on ai system design interviews for applied AI engineering.
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Agent loop guardrails
A practical note on agent loop guardrails for applied AI engineering.
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Anthropic comparison
A practical note on anthropic comparison for applied AI engineering.
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Caching LLM responses
A practical note on caching llm responses for applied AI engineering.
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Choosing between sync APIs and background jobs
Move heavy ingestion and evaluation work off the request path once latency or reliability starts to matter.
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Chunking strategies
A practical note on chunking strategies for applied AI engineering.
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Chunking strategies for product-grade retrieval
Choose chunk sizes and boundaries to preserve meaning, support citations, and improve ranking.
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Citations are a UX feature, not a footnote
Citations help the user calibrate trust, inspect weak answers, and continue research rather than starting over.
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DSPy overview
A practical note on dspy overview for applied AI engineering.
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Data quality checks
A practical note on data quality checks for applied AI engineering.
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Debug retrieval before changing prompts
Weak answers often come from bad context selection, so inspect retrieval traces before rewriting prompts.
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Deployment readiness for LLM features
Production readiness means secrets, retries, caching, tracing, and rollback plans are all intentional.
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Deployment runbooks
A practical note on deployment runbooks for applied AI engineering.
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Design your portal like a product, not a notebook
Treat content structure, navigation, and progress loops as product decisions that should reduce friction over time.
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Embedding model selection
A practical note on embedding model selection for applied AI engineering.
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Evaluation metrics
A practical note on evaluation metrics for applied AI engineering.
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Evaluation metrics that actually help iteration
Use metrics that point to specific failure modes rather than one vague quality score.
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Experiment tracking
A practical note on experiment tracking for applied AI engineering.
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Failure modes worth logging explicitly
Log missing context, schema mismatches, provider failures, judge disagreement, and human override reasons.
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Fine-tuning vs prompting vs RAG
A practical note on fine-tuning vs prompting vs rag for applied AI engineering.
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Hallucination containment
A practical note on hallucination containment for applied AI engineering.
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How to review an AI feature after launch
Post-launch review should inspect traces, evaluation drift, user pain points, and operational cost together.
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How to think about provider lock-in
Provider portability matters most at the boundary layer where schemas, retries, and cost controls are centralized.
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Human review is part of the system
Human checkpoints are a strength when confidence is low or business impact is high.
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Human-in-the-loop review
A practical note on human-in-the-loop review for applied AI engineering.
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Inference cost control
A practical note on inference cost control for applied AI engineering.
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Interview prep should mirror shipped work
The best prep material points back to projects, metrics, and tradeoffs you actually worked through.
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LangChain tradeoffs
A practical note on langchain tradeoffs for applied AI engineering.
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LangGraph overview
A practical note on langgraph overview for applied AI engineering.
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Latency budgeting
A practical note on latency budgeting for applied AI engineering.
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Latency budgets shape product design
Perceived speed depends on retrieval, provider calls, streaming behavior, and how the UI acknowledges work in progress.
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LlamaIndex use cases
A practical note on llamaindex use cases for applied AI engineering.
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Model routing
A practical note on model routing for applied AI engineering.
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Observability for AI requests
Trace context assembly, provider calls, outputs, and failures so AI bugs become debuggable engineering work.
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Open source serving
A practical note on open source serving for applied AI engineering.
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OpenAI platform notes
A practical note on openai platform notes for applied AI engineering.
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Personal knowledge bases need freshness rules
Every content library should say what is stable, what expires, and what gets reviewed on a schedule.
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Portfolio storytelling
A practical note on portfolio storytelling for applied AI engineering.
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Prompt evaluation needs qualitative notes too
Scores alone miss tone, structure, and user trust; keep notes that explain why a version won or failed.
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Prompt safety basics
A practical note on prompt safety basics for applied AI engineering.
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Prompt template design
A practical note on prompt template design for applied AI engineering.
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Prompt templates should behave like contracts
Treat prompts as explicit interfaces with versioning, variable boundaries, and review criteria rather than magic strings.
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Provider wrappers should be boring
A good provider wrapper normalizes responses, centralizes retries, and makes the rest of your system simpler.
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Retrieval failure analysis
A practical note on retrieval failure analysis for applied AI engineering.
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Retrieval metadata is a product decision
Metadata design decides what the system can later filter, cite, and explain back to the user.
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Serving architecture
A practical note on serving architecture for applied AI engineering.
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Synthetic evaluation sets
A practical note on synthetic evaluation sets for applied AI engineering.
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Tool calling basics
A practical note on tool calling basics for applied AI engineering.
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Tracing and observability
A practical note on tracing and observability for applied AI engineering.
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Use benchmark regressions to drive weekly work
Weekly iteration improves when regressions produce concrete follow-up tasks instead of generic worry.
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Use learning systems to support real projects
The portal should feed execution: learn, apply, review, and convert the result into portfolio evidence.
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Vector database tradeoffs
A practical note on vector database tradeoffs for applied AI engineering.
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What a strong AI project write-up includes
A strong write-up explains the problem, architecture, tradeoffs, evaluation method, and what you would improve next.
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What is RAG
A practical note on what is rag for applied AI engineering.
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What makes a RAG system trustworthy
A practical framework for grounding, citations, retrieval transparency, and evaluation in RAG products.
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When to persist generated artifacts
Persist prompts, contexts, scores, and outputs when they help review, replay, or explain product behavior.
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Why evaluation sets should start small
A curated, trusted benchmark set beats a larger but noisy dataset when you are still learning what failure looks like.
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Why many agent demos fail in production
Open-ended loops hide state, cost, and failure reasons unless you add explicit boundaries and observability.
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