搜索“RAG”

parse, rather than getting lost in the details of how to parse. With cleaner configuration, structured outputs, and new llama-cloud SDKs for Python and TypeScript, you can now leverage LlamaParse v2's enhanced parsing quality with significantly less complexity. And if you're currently using v1, don't worry: we're maintaining full support via the old llama-cloud-services SDK for the time being, while making v2 the recommended path forward for all new projects. New LlamaParse SDKs Our SDKs are the

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value your pipeline extracts, proving exactly where on the page it came from. Most standard document parsers expose coarse, layout-level bounding boxes that can tell you where a paragraph sat or circle an entire table block. But if you were parsing a high-density financial report with 20 different payroll figures or inventory values tightly packed onto a single text line, hovering over an AI’s citation would awkwardly highlight half the page. That level of precision is "good enough" for basic LL

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00 layouts you templated is worth very little when layout 201 walks in and the rate drops to zero. A template library doesn't degrade gracefully. It works perfectly inside its coverage and not at all outside it, and the edge of that coverage moves every time a vendor changes anything. Machine-learning document classification helps here, and it's the pitch you'll hear from most modern IDP vendors. A classifier can recognize a layout and pick the right template automatically instead of making you

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Follow us on The C3 Voice Assistant is my latest project aimed at making Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) applications more accessible. This voice-activated assistant caters to a broad audience, including those facing typing challenges or accessibility issues. Explore our free and paid plans today. Learn more Voice Activation: Initiated by saying “C3.” Alternatively, users can click the blue ring to activate the listening mode of the app. The wake word “C3” is

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mounts. This allowed systems to populate fields in downstream workflows, but only for predefined entity types and predictable formats Together, these tools enabled functional but fragile automation. Each new document type required extensive rule writing, custom model training, and a steady stream of annotated examples. The resulting pipelines were brittle and hard to maintain. They struggled with small layout changes, required frequent retraining, and produced low pass through rates that eroded

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onal OCR? Legacy OCR tools are useful for extracting text from simple documents. But financial compliance workflows usually need much more than raw text. Traditional OCR is often fragile. It depends heavily on templates, rules, and predictable layouts. When a document changes format, even slightly, the extraction pipeline can break. It is also usually text-first, not structure-first. OCR may detect characters on a page, but it does not reliably understand that a table continues across multiple p

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eract text dump and it has a flattened document to reason over, because no amount of reasoning rebuilds a table that stopped being a table two steps earlier. The same ceiling caps RAG pipelines, where retrieval can't exceed what extraction preserved. These three share a property that makes them useful test cases. The parse is easy, the typography is clean, and the schema will still be wrong. Credit Agreements and Lease Abstraction Defined-terms sections, amendments and riders that supersede the

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valuable if they connect to action. The final stage routes the classified, tagged document to the appropriate workflow: an approval queue, an ERP system, a legal review tool, a storage location, or a processing pipeline. This is where the integration layer matters. A classification system that produces accurate outputs but can't push them into your actual systems creates a manual handoff that negates much of the value. End-to-end agentic document processing, where classification feeds directly i

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itional IDP: What's the Difference?; The Architecture of an Agentic Document Workflow; The Brain: Large Language Models for Reasoning and Planning; The Memory: Knowledge Bases and RAG; The Tools: APIs, ERPs, and External Systems; The Output: Structured Data Ready for Downstream Automation; High-Value Use Cases: From Legal to Finance; Legal Documents and Contract Review; Financial Statements and Investment Analysis; Complex Business Onboarding: The Multi-Document Problem; Overcoming the Challenge

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rameworks/etc. these days will have some kind of MCP support. Whether its some kind of JSON you configure locally, a special class you import in a framework, or elsewhere, we encourage users to use this docs MCP server anywhere they work. “How do the MCP tools actually work?” To start with, our documentation is a dynamically generated Astro and Starlight website, built from docs across many repositories. This is hosted and deployed onto Vercel. Conveniently, Vercel provides a nice createMCPHandl

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le set of financial docs (are they public filings, reports), along with previous due diligence reports, in order to understand the business requirements. It would also need to leverage these modules during the generated workflow. Over the past few months, we’ve been exploring ways to bridge this gap. Here are three complementary patterns for adding document intelligence to Claude Code. Each approach has their own tradeoffs, and we describe them in detail so you can pick and choose which one(s) t

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verification API should deliver. The Income Verification API Landscape There are several categories of income verification solution, each with a different approach, different coverage, and different limitations. Understanding the landscape is important because no single approach covers every income type well. Payroll API Connections Payroll connectivity providers like Argyle and Pinwheel connect directly to payroll systems and HR platforms to retrieve income data programmatically. When a user g

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the assumption that a function call corresponds to a single LLM call — so long as you respect the interface of f:X→Yf: \mathbb{X} \to \mathbb{Y}f:X→Y — enabling the ability to leverage complex inference-time optimizations (like the OpenAI o1 family of models). After you’ve used one or more of these techniques to generate a new policy, you’ll want to use historical data to backtest and find out if it’s good or not. These are evals, but have been studied for decades under the name offline policy e

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Fine-Tuning for o4-mini on terminal-benchThe script below demonstrates how to fine-tune o4-mini on terminal-bench using TensorZero’s programmatic API; Deploy with confidence: Leverage the experimentation features to gradually route traffic to your fine-tuned models, starting with a small allocation and scaling based on performance metrics So, is reinforcement fine-tuning worth it? For most use cases, not yet. At 100-700x the cost of supervised fine-tuning, we only observed RFT to deliver clear

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n for each environment at a given bin corresponds to the height of its colored segment. The increase in prompt length is primarily due to the demonstrations: either because the average number of turns in a successful episode increases (BabyAI PickUpSeqGoTo) or because environment observations and agent actions are more verbose (HoVer and τ-bench Retail). This observation motivates our experimentation with a larger model (GPT-4o instead of GPT-4o Mini) with larger context capacity for the τ-bench

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ted value for production decisions that hinge on judging a single output (e.g. guardrails); However, even (very) noisy evaluators can reliably tell you which agent is better on average, meaning they can still help you pick the best variant to deploy and improve it over time It’s surprisingly hard to develop reliable LLM evaluators: they’re often noisy and poorly correlated with the metrics or outcomes practitioners actually care about. Sometimes the target is directly measurable but evaluators s

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engineers edited and approved the AI-generated changelogs, those human revisions were automatically used by TensorZero to optimize future inference requests. Mark and his team leveraged TensorZero’s built-in dynamic in-context learning feature to make the system get smarter over time — without fine-tuning, labeling datasets, or writing complex pipelines. It was a human-in-the-loop learning flywheel, out of the box. Dynamic In-Context Learning (DICL) is an inference-time optimization strategy tha

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while dynamically allocating LLM inference traffic for maximum efficiency; Across a diverse set of realistic and challenging environments, adaptive experimentation reduced the average time to correctly identify the best LLM variants (prompts, models, etc.) by 37% compared to simple A/B testing Developing LLM applications involves numerous design decisions: which model to use, how to structure prompts, what temperature and sampling parameters to set, etc. While offline evaluations and synthetic

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m exhaustively evaluates every candidate subtree to guarantee the closest match. Each distance is normalized by the size of the LLM snippet, yielding a ratio in [0, 1]. We then average these ratios across all snippets in the inference, and compute the reward is then computed as 1 - average ratio. A perfect match receives a score of 1, while lower scores indicate poorer performance. Formally, say that an LLM inference outputs nnn code snippets with corresponding concrete syntax trees I={t1,…,tn}I

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arning processes more computationally tractable, but also enable the discovery of hidden connections and similarities that might be obscured in the original representation. By leveraging pre-trained embeddings and fine-tuning them with task-specific data, we can create models that are more accurate and require less training time compared to learning from scratch. This approach has been particularly successful in natural language processing tasks, such as sentiment analysis, named entity recognit

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