Introduction: Prompts are the interface between your application and LLMs. As applications grow, managing prompts becomes challenging—they’re scattered across code, hard to version, and difficult to test. A prompt template system brings order to this chaos. It separates prompt logic from application code, enables versioning and A/B testing, and makes prompts reusable across different contexts. […]
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Document Processing with LLMs: Enterprise Parsing, Chunking, and Extraction (Part 2 of 2)
Introduction: Processing documents with LLMs unlocks powerful capabilities: extracting structured data from unstructured text, summarizing lengthy reports, answering questions about document content, and transforming documents between formats. However, effective document processing requires more than just sending text to an LLM—it demands careful parsing, intelligent chunking, and strategic prompting. This guide covers practical document processing patterns: […]
Read more →Knowledge Graphs with LLMs: Building Structured Knowledge from Text
Introduction: Knowledge graphs represent information as entities and relationships, enabling powerful reasoning and querying capabilities. LLMs excel at extracting structured knowledge from unstructured text—identifying entities, relationships, and attributes that can be stored in graph databases. This guide covers building knowledge graphs with LLMs: entity and relation extraction, graph schema design, populating Neo4j and other graph […]
Read more →LLM Output Parsing: From Raw Text to Typed Objects
Introduction: LLMs generate text, but applications need structured data. Parsing LLM output reliably is surprisingly tricky—models don’t always follow instructions, JSON can be malformed, and edge cases abound. This guide covers robust output parsing strategies: using JSON mode for guaranteed valid JSON, Pydantic for type-safe parsing, handling partial and streaming outputs, implementing retry logic for […]
Read more →Agent Tool Selection: Building AI Agents That Choose and Use the Right Tools
Introduction: AI agents become powerful when they can use tools—searching the web, querying databases, calling APIs, executing code. But tool selection is where many agent implementations fail. The agent might choose the wrong tool, call tools with incorrect parameters, or get stuck in loops trying tools that won’t work. This guide covers practical patterns for […]
Read more →Document Processing with LLMs: From PDFs to Structured Data (Part 1 of 2)
Introduction: Documents are everywhere—PDFs, Word files, scanned images, spreadsheets. Extracting structured information from unstructured documents is one of the most valuable LLM applications. This guide covers building document processing pipelines: extracting text from various formats, chunking strategies for long documents, processing with LLMs for extraction and summarization, and handling edge cases like tables, images, and […]
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