Retrieval-Augmented Generation (RAG) has taken the AI world by storm. It's the go-to architecture for grounding Large Language Models (LLMs) in factual, private, or up-to-date data, effectively reducing hallucinations and making AI applications genuinely useful. But as many developers are discovering, building a robust, production-grade RAG pipeline is far from simple.
The standard RAG model, while powerful, is often plagued by challenges:
What if you could bypass these problems entirely? What if you could replace the complex, brittle "Retrieval" and "Augmentation" steps with a single, intelligent API call?
This is where research.do changes the game. It's not just a retrieval tool; it's Information Retrieval as a Service, designed to supercharge your AI workflows.
Let's quickly visualize a typical RAG workflow:
User Query -> Embed Query -> Vector Search -> Retrieve Raw Chunks -> Stuff into LLM Prompt -> Generate Answer
The weak link is the "Retrieve Raw Chunks" step. The quality of your entire application hinges on the hope that your vector search returns the exact, necessary, and sufficient information. But reality is messy. Sources can be contradictory. The most vital data point might be buried deep in a document. Your LLM is left to make sense of the chaos.
The Challenge: Standard RAG delivers data fragments. High-quality AI applications need synthesized knowledge.
research.do introduces a new component: an AI research agent that handles the heavy lifting of finding, analyzing, and synthesizing information before it ever reaches your LLM.
The new, streamlined architecture looks like this:
User Query -> research.do API Call -> **Receive Synthesized Report** -> Feed to LLM -> Generate Verifiable, High-Quality Answer
Instead of feeding your LLM raw, messy data, you provide it with a perfectly structured, pre-synthesized report complete with citations. Your LLM's job becomes exponentially easier: it's no longer a frazzled researcher but a brilliant communicator, summarizing and presenting the already-verified findings.
Implementing this is incredibly simple. research.do abstracts away the complexity of multi-source querying and analysis into one API call.
import { createDo } from '@do-sdk/client';
// Initialize the research.do client with your API key
const research = createDo('research.do');
// Ask a complex question and specify your sources
const report = await research.query({
question: "What are the latest advancements in quantum computing and their potential impact on cryptography?",
sources: ["arxiv", "google-scholar", "web"],
depth: "comprehensive",
format: "summary_report"
});
// The 'report.summary' is now the perfect, rich context for your LLM
console.log(report.summary);
// Your LLM can now use this summary to generate a final answer
// const finalAnswer = await myLLM.generate(report.summary);
Fusing research.do into your RAG pipeline isn't just an incremental improvement; it's a fundamental shift that delivers powerful benefits.
Your application is no longer limited by how recently you've updated your vector store. research.do performs live queries against public sources like the web, academic databases, and news articles. When your user asks a question, they get an answer based on today's information, not last month's.
Our AI agent doesn't just find keywords; it understands intent. It dives deep into multiple sources, cross-references facts, and synthesizes a coherent narrative. This process filters out noise and delivers a dense, highly relevant context to your LLM, dramatically improving the accuracy and depth of the final output. Each piece of information is cited, ensuring full transparency and verifiability.
Say goodbye to the engineering nightmare of building and maintaining a data ingestion pipeline. research.do handles it all. There's no need to manage vector databases, chunking algorithms, or indexing schedules. You can focus your resources on what matters most: the core logic and user experience of your application.
Need to build an app that understands the latest academic breakthroughs, business news, and technical documentation simultaneously? With research.do, you can. A single query can intelligently pull from sources like arXiv, Google Scholar, public websites, and even your own private data stores. This multi-source synthesis capability is what sets it apart, enabling applications that were previously impossible to build.
Whether for business or academic purposes, this enhanced pipeline unlocks new possibilities:
The next generation of AI applications won't be defined by the size of their vector database, but by the quality of the information they can access and understand. By shifting from simple data retrieval to intelligent research and synthesis, you can build more accurate, more powerful, and more reliable applications with a fraction of the effort.
Stop wrestling with data pipelines. Start building with intelligence.