Applied AI Engineering

Your journey into building production AI systems starts here

Navigate the model landscape with a reusable decision framework

Understand the engine under the hood to engineer better prompts

Master prompt management, token-driven economics, and p95 latency guarantees

Secure API secrets, build a thin client boundary, and verify integrations

Maintain consistent AI behavior, prevent overrides, and isolate constraints using layered prompt architecture

Guide label formatting with few-shot examples and steer intermediate reasoning with chain-of-thought scaffolds

Enforce strict JSON schemas and function calling to build reliable, validated AI interfaces

Treat prompts as software interfaces: property-based tests, A/B evaluation, and tracing

Transform text into high-dimensional vectors, compute cosine similarity, and master semantic retrieval

Evaluate persistence, metadata filtering, and scaling trade-offs across local and cloud vector engines

Optimize chunk size, overlap, structure-aware splitting, and lexical-dense hybrid retrieval

Implement deterministic ingestion, Chroma vector search, and structured generation with verified citations

Diagnose and fix stale indexes, bad chunking, retrieval misses, and citation drift in production

Enforce strict schema contracts, allowlists, runtime guardrails, and bounded tool budgets

Evaluate custom loops, LangChain abstractions, and stateful LangGraph workflows with explicit stop conditions

Detect infinite loops, formulate executable plans, and partition short-term context from persisted memory

Implement an end-to-end ReAct Research Assistant with Gemini 2.5 Flash, Chroma vector store, and guardrails

Diagnose production failures and match prompting, RAG, or fine-tuning to the right fix

Train lightweight LoRA and QLoRA adapters without retraining the full model

Build leak-free, boundary-safe supervision sets for reliable fine-tuning

Validate structural contracts and ship regression-free fine-tuned adapters

Turn fuzzy quality judgments into small, reproducible eval sets for grounded Q&A

Combine deterministic checks, embedding similarity, and calibrated LLM-as-judge rubrics

Design controlled experiments that separate real improvement from noise and confounders

Trace unsupported claims through the evidence chain, mitigate, and gate releases

Design stable FastAPI contracts, service boundaries, streaming, and error models

Decide what to cache, bound traffic with rate limits, and guard spend per request

Trace requests end-to-end, log safely, and instrument spans without exposing raw data

Containerize, deploy to AWS, gate on tests and evals, and scale around provider limits

Combine text and image inputs into testable, evidence-backed multimodal requests

Design predictable binary-media API contracts for generating and editing images

Build a reliable speech-to-text and text-to-speech loop with explicit boundaries

Pipe audio, text, and images through one coherent, validated request pipeline

Recognize prompt injection and jailbreaks, then block side effects before they execute

Map data flows, choose a redaction strategy, and lock down logs, traces, and storage

Turn bias complaints into paired evals and apply the smallest effective mitigation

Layer enforcement, choose a moderation strategy, and verify guardrails in production

You completed Applied AI Engineering — here is your recap and what comes next