🦄 PDFs, Multimodality, Vision Models
Dive deep into practical PDF processing techniques for AI applications. We'll explore how to extract, parse, and leverage PDF content effectively in your AI workflows, tackling common challenges like layout preservation, table extraction, and multi-modal content handling.
Project Details
Open in GitHubAI That Works #15: PDFs, Multimodality, Vision Models
Practical techniques for processing PDFs with multimodal AI - from image preprocessing to structured data extraction
Episode Highlights
In this episode, we explored how to effectively process PDF documents using multimodal AI models. We tackled the challenge that models don't read PDFs natively but convert them to images, and demonstrated how to take control of this process for better results.
Key Topics
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PDF Processing with Multimodal LLMs: Understanding that models don't read PDFs natively but convert them to images and OCR text, and the implications of this hidden pre-processing step.
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Image Tokenization: A conceptual model for how images are broken into tokens and how image resolution and content density affect model performance for summarization vs. detail-oriented tasks.
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Deterministic Pre-processing: Using standard image processing libraries (like Pillow/OpenCV) to solve parts of the problem without an LLM, such as reliably detecting and removing common headers and footers from document pages.
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Pipeline Accuracy and Runtime Evals: The concept that multi-step AI pipelines have compounding failure rates and the strategy of using deterministic checks (e.g., summing transactions) to validate LLM output in real-time.
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Handling Edge Cases: Practical techniques for solving common document processing challenges, such as parsing records that are split across a page break by providing cropped context from the previous page.
Whiteboards
All Episodes
Voice Agents and Supervisor Threading
Exploring voice-based AI agents and supervisor threading patterns for managing complex conversational workflows.
Claude for Non-Code Tasks
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Interruptible Agents
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Decoding Context Engineering Lessons from Manus
A few weeks ago, the Manus team published an excellent paper on context engineering. It covered KV Cache, Hot-swapping tools with custom samplers, and a ton of other cool techniques. On this week's episode, we'll dive deep on the manus Article and put some of the advice into practice, exploring how a deep understanding of models and inference can help you to get the most out of today's LLMs.
Context Engineering for Coding Agents
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Evaluating Prompts Across Models
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Implementing Decaying-Resolution Memory
Last week on #13, we did a conceptual deep dive on context engineering and memory - this week, we're going to jump right into the weeds and implement a version of Decaying-Resolution Memory that you can pick up and apply to your AI Agents today. For this episode, you'll probably want to check out episode #13 in the session listing to get caught up on DRM and why its worth building from scratch.
Building AI with Memory & Context
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Boosting AI Output Quality
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Building an AI Content Pipeline
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Entity Resolution: Extraction, Deduping, and Enriching
Disambiguating many ways of naming the same thing (companies, skills, etc.) - from entity extraction to resolution to deduping. We'll explore breaking problems into extraction → resolution → enrichment stages, scaling with two-stage designs, and building async workflows with human-in-loop patterns for production entity resolution systems.
Cracking the Prompting Interview
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Humans as Tools: Async Agents and Durable Execution
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12-factor agents: selecting from thousands of MCP tools
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Policy to Prompt: Evaluating w/ the Enron Emails Dataset
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Designing Evals
Minimalist and high-performance testing/evals for LLM applications. Stay tuned for our season 2 kickoff topic on testing and evaluation strategies.
Twelve Factor Agents
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Code Generation with Small Models
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Reasoning Models vs Reasoning Prompts
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Large Scale Classification
LLMs are great at classification from 5, 10, maybe even 50 categories. But how do we deal with situations when we have over 1000? Perhaps it's an ever changing list of categories?