Agentic AI
How to Design Reliable Agentic AI Workflows
Practical lessons from building Open Tools: bounded agents, explicit permissions, durable task memory, validation gates and deployment checks.
Read article →NOTES FROM BUILDING
AI products, data systems
and the engineering behind them.
Practical lessons from products I have designed and built across agentic AI, evidence intelligence, RAG, SaaS, CRM, document generation and technical web systems.
RAG & Search
RAG for Evidence-Heavy Businesses: Source-Linked Retrieval That People Can Trust
How to design RAG and semantic retrieval for evidence, compliance and document-heavy workflows using source boundaries, pgvector and auditability.
Read article →AI Product Design
Human-in-the-Loop AI for Sensitive Workflows
Why AI outputs should often remain drafts until reviewed, and how structured schemas, confidence and confirmation improve trust.
Read article →AI Product Engineering
From AI Prototype to Production Product: What Changes After the Demo Works
A practical production checklist from DDDecks.ai: observability, error contracts, security, failure handling, data boundaries and deployment.
Read article →CRM & Product Design
How to Design a Relationship-Intelligence CRM Instead of Another Contact Database
Product lessons from Founder CRM: goals, people, companies, opportunities, warm paths, scoring and next actions.
Read article →Generative AI
AI Document Generation Beyond Prompt Engineering
Lessons from Instant Deck on evidence extraction, research, memo synthesis, narrative planning, visual planning and controlled rendering.
Read article →Vector Search
pgvector Semantic Search: Production Lessons from Evidence Retrieval
Practical guidance on using PostgreSQL and pgvector for semantic search, with metadata filters, ownership boundaries and retrieval evaluation.
Read article →AI Evaluation
Structured Outputs and LLM Evaluation for Production AI Products
How schemas, validation, critique, repair and regression testing improve reliability in LLM-powered workflows.
Read article →Technical SEO
SEO-Safe WordPress to SvelteKit Migration: What I Would Protect First
Lessons from migrating a large content site: route inventories, canonical URLs, indexing gates, media migration and deterministic content.
Read article →AI Strategy
Building AI Products Around Real Business Workflows
A cross-project framework for deciding where AI belongs: workflow mapping, structured context, model boundaries, human review and measurable outcomes.
Read article →Parallel AI
Parallelising Embedding Generation for Large Document Collections
How to design embedding pipelines that scale with batching, partitioning, retries, idempotency and cost-aware concurrency.
Read article →HPC & ML Systems
What Slurm and HPC Scheduling Teach Us About Production ML
Lessons from scientific HPC that apply directly to batch inference, evaluation, document processing and AI pipelines.
Read article →Machine Learning
DBSCAN vs K-Means: Choosing a Clustering Model for Real Product Questions
A practical comparison of DBSCAN and K-means based on safeguarding and marketplace segmentation projects.
Read article →Responsible ML
Synthetic Data for Sensitive Machine Learning: What It Can and Cannot Prove
How synthetic data can support privacy-preserving prototyping without pretending simulated labels are real-world evidence.
Read article →ML Evaluation
Machine Learning Evaluation Beyond Accuracy
Why precision, recall, F1, calibration, failure costs and operational thresholds matter more than one headline metric.
Read article →Data Science
Feature Engineering for Longitudinal Behavioural Data
Practical ways to turn event-level histories into trends, volatility, rolling windows and cohort-comparable features.
Read article →Data Products
Designing an R Shiny Dashboard Around Machine-Learning Decisions
How to integrate models into interactive dashboards without overwhelming non-technical users.
Read article →Human-Centred AI
Designing Risk Dashboards for Human Review
Human-centred principles for alerting, triage and machine-learning outputs in sensitive workflows.
Read article →RAG Evaluation
How to Evaluate a RAG System: Retrieval, Grounding and Product Quality
A practical RAG evaluation framework covering retrieval recall, citation correctness, unsupported claims, latency and cost.
Read article →Search Engineering
Hybrid Search: When Vector Similarity Is Not Enough
Why production search often combines semantic retrieval, lexical matching and metadata filters.
Read article →RAG & Search
Reranking for RAG: Improving Relevance After Retrieval
How reranking can improve retrieval quality without replacing your vector database.
Read article →Agentic AI
Observability for AI Agents: What to Log Without Creating a Privacy Problem
A practical observability model for agent runs, tool calls, retries, failures and sensitive data boundaries.
Read article →AI Evaluation
Regression Testing Prompts and LLM Workflows
How to stop prompt edits and model upgrades from silently breaking production behaviour.
Read article →Production AI
Model Routing: Balancing AI Quality, Cost and Latency
How to route requests across models based on task complexity, risk and response-time requirements.
Read article →Production Engineering
Structured Logging for Production AI Applications
How request IDs, event names and redaction make AI systems easier to debug and safer to operate.
Read article →AI Systems
Background Jobs for Long-Running AI Workloads
Why document processing, report generation and multi-step AI tasks should often move out of the request-response cycle.
Read article →AI Product Architecture
Designing PostgreSQL Data Models for AI Products
Why durable AI products need explicit entities, source records, artifacts and model-run metadata—not just chat history.
Read article →Technical Product
Entity Modelling for AI Product Managers
How entities and relationships turn vague AI features into buildable product specifications.
Read article →Technical Product Management
Roadmapping AI Products Under Uncertainty
How to prioritise platform capability, configuration and experiments when model capability and customer needs move quickly.
Read article →Enterprise AI
AI POC to Enterprise: A Practical Product Checklist
What has to change when an AI proof of concept becomes a real multi-user product.
Read article →Data Visualization
Tableau Dashboard Design Lessons from 97,224 Airbnb Listings
What a large London marketplace dataset taught me about aggregation, filtering, coordinated views and analytical storytelling.
Read article →RAG & AI Evaluation
How to Fine-Tune and Optimise RAG Systems for High-Stakes Decision Support
A practical guide to improving RAG systems through embedding fine-tuning, FAISS optimisation, hybrid retrieval, generative-model tuning, evaluation and continuous monitoring.
Read article →RAG Fundamentals
RAG Decision Support Explained: Retrieval, FAISS and Grounded Generation
A clear explanation of how decision-support logic fits into RAG, what FAISS does, and why RAG can provide better domain-specific context than a standalone LLM.
Read article →GraphRAG & Knowledge Systems
RAG + Knowledge Graphs: How Graph-Aware Retrieval Improves Context Relevance
How combining Retrieval-Augmented Generation with knowledge graphs improves entity linking, relationship-aware retrieval, multi-hop context and grounded answers.
Read article →Agentic RAG
Agentic RAG: Multi-Step Reasoning with Retrieval and Generation
How agentic RAG extends standard retrieval-augmented generation with planning, iterative retrieval, tool use, validation and multi-step reasoning.
Read article →