Intro
I explored Doc wrangler. With the Solvit team, we had a paper reading ofDocETL and its power to steer semantic data processing. One of the questions that came up was where does a tool like DocETL fit? Is it just another way to do RAG, or is it something fundamentally different?
I think they are two entirely different beasts, designed for different philosophies of work. RAG is built to find the sharpest needle in a haystack to answer a specific question. DocETL is built to systematically parse the entire haystack to tell you everything about the needles, the hay, and how they relate.
This distinction is key. Let’s break down the differences.
The Fundamental Difference: Retrieval vs. Comprehensive Processing
The core divide between RAG and a framework like DocETL comes down to their primary objective.
Retrieval-Augmented Generation (RAG) is a real-time search and synthesis architecture. Its job is to:
Take a user’s query (“What is our company’s policy on remote work?”).
Quickly search a massive corpus of documents to find the most relevant snippets.
Feed those snippets to an LLM as context to generate a direct, concise answer.
RAG is optimized for efficiency and speed. It intentionally prunes the search space, ignoring the 99% of documents that aren’t relevant to the immediate question. Its success is measured by the quality and speed of a single answer.
DocETL’s Semantic Steering is a batch processing and data structuring architecture. Its job is to:
Take an entire dataset of documents (e.g., all 50,000 customer support tickets from last quarter).
Comprehensively process every single document through a series of defined operations (map, reduce, filter).
Transform the unstructured chaos into a perfectly structured, new dataset.
DocETL is optimized for accuracy and completeness. It doesn’t skip a single document because its goal is to create a holistic, structured view of the entire information landscape. Its success is measured by the quality of the final, enriched dataset. Its also more expensive to process but is becoming cheaper every year.
| Feature | Retrieval-Augmented Generation (RAG) | DocETL (Semantic Processing) |
|---|---|---|
| Objective | Answer a specific question | Process an entire dataset |
| Scope | Targeted Retrieval (finds relevant snippets) | Comprehensive Parsing (analyzes every doc) |
| Workflow | Real-time, per-query | Offline, batch processing |
| Economics | Optimized for low latency & cost per query | Optimized for accuracy of the final dataset |
| Primary Use | Chatbots, Q&A systems, research assistants | Knowledge base creation, large-scale analysis |
How DocETL’s Architecture Transforms Your Data Workflow
This philosophical difference leads to a profound shift in the user’s workflow. The traditional data science process is linear: clean the data, explore it, then build a model. Tools like DocETL turn this on its head, enabling what we can call “Opportunistic Modeling.”
Instead of spending weeks cleaning a dataset before you even know what’s in it, you jump straight into building an extraction pipeline. The initial, imperfect output becomes your exploratory analysis. You see what the LLM found, identify where it struggled, and use the visual interface to provide feedback.
This blurs the line between data cleaning and analysis. The LLM handles minor inconsistencies implicitly, and your focus shifts from low-level cleaning to high-level steering. You’re not just a data janitor; you’re a collaborator, guiding the AI to a deeper understanding of the data.
The Architecture of Improvement: Inner vs. Outer Loops
This collaborative process is powered by a brilliant architectural concept: the separation of the “outer loop” and the “inner loop.”
The Outer Loop (Human-Driven): This is where you, the domain expert, operate. You review the processed data in the GUI, look at the outputs in situ, and add qualitative comments like “This summary is too vague” or “You missed the urgency in this complaint.” You are providing high-level, semantic guidance. You are steering.
The Inner Loop (AI-Driven): This is the automated optimization engine. The “LLM-as-a-judge” takes your feedback from the outer loop and works internally to refine the pipeline. It might tweak the prompt, adjust the temperature, or even suggest splitting one operation into two for better clarity. It handles the tedious work of prompt engineering based on your strategic direction.
RAG evaluation is often about the final answer’s quality. DocETL’s loop system is about improving the quality of the entire processing engine over time, making it a learning system that adapts to your specific domain and needs.
Better Together: How DocETL and RAG Can Be Allies
So, is it a competition? Not at all. In fact, they form a powerful, symbiotic relationship. The biggest weakness of many RAG systems is the quality of the source documents—the old “garbage in, garbage out” problem.
This is where the true synergy emerges:
Use DocETL First: Take your messy, unstructured data—a chaotic collection of PDFs, emails, and transcripts—and use DocETL to process it. Extract key entities, summarize long passages, categorize content, and output a clean, structured JSON or CSV file. You’ve transformed your haystack into a well-organized library of needles.
Build Your RAG System on Top: Feed this new, high-quality structured dataset into your vector database. Now, when your RAG system performs a search, it’s not wading through noise. It’s retrieving clean, relevant, and pre-processed information.
The result? Your RAG system becomes dramatically more accurate, reliable, and capable of answering more complex questions because it’s built on a foundation of clean, structured knowledge that you created and validated with DocETL.
Conclusion: The Right Tool for the Job
The conversation around AI is maturing. We’re moving beyond the idea of a single, monolithic “AI” that can do everything and toward a sophisticated toolkit of specialized architectures.
RAG is an incredible tool for real-time information retrieval. But for the deep, comprehensive work of turning unstructured data into a valuable asset, a systematic processing framework is essential. DocETL shows us what that looks like—a collaborative, optimizable, and scalable way to make sense of the information chaos. The real magic happens when we realize they’re not competitors, but partners in building the next generation of intelligent applications.