>"Enterprise Document Intelligence – A fixed BASE, the rules each question needs, one registry: the dispatcher that turns a parsed question into a typed LLM call"
Instead of "mega-prompts," use a Dispatcher Pattern to assemble a `BASE` prompt with specific fragments (shape and constraints) at runtime. This improves accuracy, simplifies maintenance, and aids auditing.
* Modular Prompting: Uses "shape fragments" (formatting/extraction) and "constraint fragments" (specific rules).
* Execution Modes: Combined (sends all chunks at once) vs sequential (Iterative chunk processing to save costs)
* Structural Scoping: Uses query hints (e.g., page numbers) to refine retrieval.
* Best Practices: Use Temperature 0, maintain a 20–30% context window buffer, and log raw model responses.
Instead of "mega-prompts," use a Dispatcher Pattern to assemble a `BASE` prompt with specific fragments (shape and constraints) at runtime. This improves accuracy, simplifies maintenance, and aids auditing.
* Modular Prompting: Uses "shape fragments" (formatting/extraction) and "constraint fragments" (specific rules).
* Execution Modes: Combined (sends all chunks at once) vs sequential (Iterative chunk processing to save costs)
* Structural Scoping: Uses query hints (e.g., page numbers) to refine retrieval.
* Best Practices: Use Temperature 0, maintain a 20–30% context window buffer, and log raw model responses.
Context engineering shifts RAG focus from prompt tuning to structured data assembly for LLM calls. The single-document architecture utilizes four bricks—parsing, question parsing, retrieval, and generation—to produce typed context pieces. These include system prompts, filtered document segments, and structured metadata. This engineering discipline improves auditability, enables caching, and supports scalable component composition.
- Four-brick pipeline: parsing, question parsing, retrieval, generation
- Typed data outputs for LLM context assembly
- Fixed system prompts for caching efficiency
- Filtered document lines and structured metadata
- Improved auditability and cost control
- Four-brick pipeline: parsing, question parsing, retrieval, generation
- Typed data outputs for LLM context assembly
- Fixed system prompts for caching efficiency
- Filtered document lines and structured metadata
- Improved auditability and cost control
This article explores the capacity of Vision Language Models (VLMs) to serve as advanced document parsers. It addresses the limitations of traditional text extraction methods when encountering visual elements like charts, diagrams, and tables within PDFs. By leveraging vision capabilities, these models enable more effective Retrieval-Augmented Generation (RAG) systems by interpreting multimodal content that is typically lost in standard text parsing workflows.
* Limitations of conventional PDF text extraction
* Capabilities of VLMs in understanding visual data structures
* Enhancing RAG pipelines through multimodal document analysis
* Limitations of conventional PDF text extraction
* Capabilities of VLMs in understanding visual data structures
* Enhancing RAG pipelines through multimodal document analysis
This article examines Docling, a tool from IBM Research that converts complex PDF documents into structured Markdown or JSON for RAG applications. It offers a local-first approach to ensure data privacy and provides high-fidelity extraction of rich tables and layouts without relying on cloud services.
* **Problem:** LLMs struggle to derive reliable meaning from raw sensor signals, often producing non-actionable or factually incorrect interpretations of time-series data.
* **Methodology:** The study implements a structured RAG-based prompt structure that combines water consumption measurements with descriptive statistics and qualitative user information (such as household water practices).
* **Key Finding:** Augmenting prompts with multidimensional contextual information leads to much higher evaluation scores for grounding, pattern recognition, and actionable recommendations.
* **Methodology:** The study implements a structured RAG-based prompt structure that combines water consumption measurements with descriptive statistics and qualitative user information (such as household water practices).
* **Key Finding:** Augmenting prompts with multidimensional contextual information leads to much higher evaluation scores for grounding, pattern recognition, and actionable recommendations.
This research investigates ways to help large language models interpret time-series sensor data by augmenting measurements with statistical summaries, detected patterns, and environmental context. The study evaluates baseline LLMs, fine-tuned models, and retrieval-augmented generation approaches, finding that combining specialized training with contextual information significantly improves grounding, actionability, and pattern recognition while reducing hallucinations.
* Augmenting time-series data with social and environmental context
* Comparing RAG frameworks against baseline and fine-tuned LLMs
* Enhancing the reliability of automated sensor monitoring systems
* Augmenting time-series data with social and environmental context
* Comparing RAG frameworks against baseline and fine-tuned LLMs
* Enhancing the reliability of automated sensor monitoring systems
This article examines why basic text extraction from PDFs often falls short when building Retrieval Augmented Generation (RAG) pipelines. It highlights how losing visual layout information results in lost semantic context, affecting model accuracy and retrieval performance. The author introduces the concept of two critical layers within a document: the physical layer involving raw character data and coordinates, and the logical layer that constructs meaning through structural elements like headings, tables, and multi-column layouts.
- Why standard text extraction limits RAG performance
- Understanding physical versus logical PDF layers
- The role of layout awareness in preserving semantic context
- Why standard text extraction limits RAG performance
- Understanding physical versus logical PDF layers
- The role of layout awareness in preserving semantic context
Unlike cloud AI services like Claude or Gemini, local LLMs lack built-in workspace features for persistent memory. You can bridge this gap using "context journaling" via system prompts and RAG.
* LM Studio presets for concise system prompts.
* RAG document uploads for background/project history.
* Markdown journal structure (Background, Projects, Corrections).
* “Corrections” section to prevent recurring model errors.
* Session exports for prompt effectiveness records.
* LM Studio presets for concise system prompts.
* RAG document uploads for background/project history.
* Markdown journal structure (Background, Projects, Corrections).
* “Corrections” section to prevent recurring model errors.
* Session exports for prompt effectiveness records.
This article explores techniques for optimizing Retrieval-Augmented Generation (RAG) systems by implementing hybrid search and re-ranking mechanisms. It details how to combine dense vector embeddings with sparse keyword matching, such as BM25, to improve retrieval accuracy, followed by the use of a cross-encoder reranker to ensure only the most relevant context is passed to a Large Language Model in production environments.
Memori is an agent-native memory infrastructure that acts as an LLM-agnostic layer to transform AI agent execution and conversations into structured, persistent state for production systems. It integrates seamlessly into existing architectures, allowing agents to automatically capture and recall information from past interactions without requiring changes to core code or prompts.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
Key features and points:
* Provides advanced augmentation of memories including attributes, facts, preferences, relationships, and skills at the entity, process, and session levels.
* Achieves high accuracy and token efficiency in long-conversation memory as demonstrated by LoCoMo benchmark results.
* Offers dedicated SDKs for both Python and TypeScript.
* Supports Model Context Protocol (MCP) for easy connection to developer tools like Claude Code and Cursor.
* Compatible with a wide range of LLMs including OpenAI, Anthropic, Gemini, DeepSeek, and Grok, as well as frameworks like LangChain and Pydantic AI.
