Guides

Train AI agents with trajectory tracking and holistic evaluation.

Build RFT pipelines with state management, grading systems, and production mirroring.

References: Reinforcement learning, FinQA benchmarks

Coordinate autonomous dev workflows with review-ready checkpoints.

Ship an event-driven agent that hands off code for human review in under an hour.

References: DSPy playbooks, GitHub Actions

Define, design, and orchestrate LLM-powered agents.

Design multi-agent orchestration with shared memory and evaluation hooks.

References: ReAct, AutoGPT lessons

Nine pillars for thriving in an AI-first engineering era.

Develop a personal roadmap that balances automation with human agency.

References: MIT Future of Work reports

A practical guide to working smarter with AI.

Install a daily workflow for prompt design, iteration, and evaluation.

References: OpenAI & Anthropic best practices

A practical framework focused on data quality for robust AI systems.

Audit datasets, write eval-ready schemas, and prioritize data feedback loops.

References: Andrew Ng Data-Centric AI

Short, practical guide to DSPy programming and GEPA optimization.

Compose DSPy modules that optimize prompts automatically against evals.

References: Stanford DSPy docs

Evaluate AI systems with practical frameworks and examples.

Stand up automated eval harnesses that guard-rail agents before launch.

References: Helm, GAIA, custom rubric templates

Adapt open-source models to your specific domain with a structured process.

Run small-batch fine-tuning with evaluation gates and rollback plans.

References: LoRA, QLoRA, PEFT

Master the foundations of Large Language Models - architecture, training, and applications.

Understand transformer internals, scaling laws, and inference constraints.

References: Attention Is All You Need, Chinchilla scaling

Build knowledge-intensive apps by combining LLMs with retrieval.

Ship a retrieval-augmented pipeline with evaluation guardrails and analytics.

References: LangChain, LlamaIndex, vector DB benchmarks

Build powerful tool-enabled agents using MCP.

Wire MCP servers, capabilities, and sessions into your production agent.

References: Anthropic MCP spec

Patterns, techniques, and practical prompts for real-world systems.

Apply structure, context windows, and evals to ship resilient prompts.

References: Chain-of-thought, Tree-of-thought

A structured walkthrough for crafting reliable prompts.

Follow a repeatable checklist to debug and version prompts quickly.

References: Versalist prompt library