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Loop Engineering for RAG Generation: An LLM Cascade from a Cheap Local Model Up to a Hosted Flagship

typed fields to pull out of a stack of real documents: amounts, dates, coverage limits, one structured value per field. The default reflex is to send…

Most RAG Hallucinations Are Extraction Errors: Seven Patterns for a Typed Generation Contract

the same shape of answer. One wants a single number with a citation, another a list with one item per row, a third a yes or…

Loop Engineering for RAG Generation: iterate top-k one at a time

The user asks “what is the effective date of this policy?”. Retrieval returns five candidate line-windows and passes them all to the LLM in one prompt.…

Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation

talk about context engineering today, they usually mean retrieving the right context from a document: chunk selection, hybrid search, reranking, TOC-aware retrieval. That is one half…

Assemble Each RAG Generation Prompt from a Base Prompt Plus the Rules Each Question Needs

part of the generation brick of Enterprise Document Intelligence, a series that builds an enterprise RAG system from four bricks: document parsing, question parsing, retrieval, and…

Designing a Schema-Guided Invoice Intelligence Pipeline with lift-pdf for Accounts-Payable Extraction, Validation, and Ledger Generation

def render_pdf(d, path): “””Draw a realistic one-page invoice: header, meta, bill/ship, line items, totals, payment.””” from reportlab.lib.pagesizes import LETTER from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units…

DeepSeek Releases DSpark, a Speculative Decoding Framework That Accelerates DeepSeek-V4 Per-User Generation 60–85% Over MTP-1

DeepSeek released DSpark, a speculative decoding framework, with open-source checkpoints and training code. It is a serving optimization, not a new model. The checkpoints DeepSeek-V4-Pro-DSpark and…

RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation

Tquestion-parsing brick of Enterprise Document Intelligence, a series that builds an enterprise RAG system from four bricks: parsing, question parsing, retrieval, and generation. Question parsing is…

Google AI Releases DiffusionGemma, a 26B MoE Open Model Using Text Diffusion for Up to 4x Faster Generation

Google AI team including the Google DeepMind researchers have just released DiffusionGemma, an experimental open model for text generation. It uses text diffusion instead of standard…

NVIDIA Releases Cosmos 3: A Two-Tower Mixture-of-Transformers Foundation Model Unifying Physical Reasoning, World Generation, and Action Generation

NVIDIA AI team have released Cosmos 3. It is a family of omnimodal world models for physical AI. The models combine physical reasoning, world generation, and…

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