// Blog & Research

Notes from the field

What we're learning building and shipping production AI — research, teardowns, and the occasional strong opinion.

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Manish Babbar · Jun 2, 2026

Agentic AI Workflow Orchestrator

A visual, modular AI-agent builder — compose agents from drag-and-drop blocks for decision-making, research and action, backed by Pydantic and OpenAI/Groq LLMs with parallel tas…

Case StudyAgentic AILLMsAutomationRead →

Lightning Leap · Jun 1, 2026

Why we build AI Accelerators, not just models

AI accelerators turn raw compute into predictable, cost‑effective performance, letting data teams ship models faster and scale sustainably.

AIMLOpsEngineeringRead →

Lightning Leap · May 18, 2026

RAG that doesn't hallucinate: grounding is a pipeline, not a prompt

Hallucinations in Retrieval‑Augmented Generation vanish when grounding is treated as a disciplined data‑pipeline rather than a clever prompt hack.

RAGLLMEngineeringRead →

Lightning Leap · May 4, 2026

Fine-tune or prompt? A decision framework that fits on a napkin

When to invest in model fine‑tuning versus clever prompting is a trade‑off of data, latency, and maintainability—this guide gives you a quick, actionable matrix.

LLMFine-tuningStrategyRead →

Lightning Leap · Apr 20, 2026

ETL pipelines that don't rot

ETL pipelines crumble when they’re treated as one‑off scripts; robust design, observability, and modularity keep data flowing reliably for years.

DataETLEngineeringRead →

Lightning Leap · Apr 6, 2026

Evals before vibes: how we know an LLM feature actually works

In the hype‑driven AI market, rigorous evaluation— not gut feeling— is the only way to certify that a new LLM capability delivers real value.

EvalsLLMQualityRead →

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