The Blog
Thoughts, deep dives, and practical guides on AI engineering, large language models, and building intelligent systems.
Agentic Workflows in Fintech: Orchestrating LLM Agents for Autonomous Decision-Making
How autonomous AI agents are transforming financial services — from loan underwriting to fraud investigation — and the architectural patterns that make them production-ready.
Prompt Engineering for Production: Beyond Basic Prompts
Moving past toy prompts — a systematic guide to prompt design patterns, reliability techniques, and testing strategies for production LLM applications.
Vector Databases Demystified: Choosing the Right One for Your AI Stack
A practical comparison of Pinecone, Weaviate, Qdrant, pgvector, and Chroma — covering indexing algorithms, performance tradeoffs, and when to use each.
MLOps in 2026: The Toolchain That Actually Works
A pragmatic overview of the modern MLOps stack — from experiment tracking and model registry to serving, monitoring, and retraining pipelines.
Scaling LLM Inference at Enterprise Scale: Lessons from Production
A practitioner's guide to optimizing LLM inference for high-throughput, low-latency enterprise workloads — covering quantization, batching, caching, and speculative decoding.
Building a Production-Grade Text-to-SQL System
How to build a reliable natural language to SQL engine: schema awareness, query validation, error recovery, and safety guardrails for enterprise databases.
Transformer Attention Mechanisms: From Self-Attention to Flash Attention 3
A deep dive into transformer attention — the math, the memory bottleneck, and how Flash Attention 3 achieves 1.5–2x speedups through hardware-aware algorithm design.
Fraud Detection with Graph Neural Networks: Beyond Tabular Features
How graph neural networks capture transaction relationships that tabular ML misses — architecture, feature engineering, and deployment patterns for real-time fraud detection.
RAG Beyond the Basics: Advanced Retrieval Strategies for Financial Documents
Moving past naive RAG — advanced chunking, hybrid search, reranking, and evaluation strategies for building retrieval systems that work on complex financial documents.
Context Window Engineering: Making the Most of Long-Context LLMs
Practical strategies for working with 128K–1M token context windows — retrieval vs. stuffing tradeoffs, context compression, position bias, and structured context packing.
Responsible AI in Financial Services: From Principles to Practice
How to operationalize responsible AI in a regulated industry — fairness testing, model explainability, bias audits, and building the governance infrastructure that regulators actually want to see.
Evaluating LLM Systems: Metrics, Benchmarks & Human-in-the-Loop
A framework for evaluating LLM-powered systems in production — covering automated metrics, human evaluation protocols, and continuous monitoring for enterprise applications.
Multi-Agent Systems: Designing Collaborative AI Architectures
How to architect multi-agent systems where specialized LLM agents collaborate, delegate, and critique each other — covering orchestration patterns, communication protocols, and failure modes.
Embeddings in Practice: From Word2Vec to Modern Sentence Transformers
A practical guide to text embeddings — understanding the math, choosing the right model, fine-tuning for domain adaptation, and common pitfalls in production embedding pipelines.
Fine-Tuning Domain-Specific LLMs: A Practitioner's Guide for Regulated Industries
End-to-end guide to fine-tuning LLMs for domain-specific tasks in regulated industries — covering data curation, LoRA/QLoRA, evaluation, and compliance considerations.
CI/CD for Machine Learning: Automating Model Validation and Deployment
Building a proper CI/CD pipeline for ML — automated model testing, data validation, performance regression checks, and safe deployment patterns including canary releases and shadow mode.
LLM Security: Defending Against Prompt Injection and Jailbreaks
A technical guide to LLM security threats — prompt injection, indirect injection, jailbreaks, data exfiltration, and the defensive architectures that actually work in production.