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How to Build a Custom AI Research Assistant from Scratch

1. Define Your Research Workflow & Data Sources

Map out the repetitive research tasks you perform daily (e.g., gathering news, summarizing papers, extracting key stats) and identify which ones can be automated.
List the specific data sources you want your assistant to pull from—RSS feeds, PDF repositories, APIs, or internal databases.
Set clear output expectations: do you need a daily digest, a tagged database, or a Q&A interface over your collected knowledge?

Select a language model (e.g., GPT-4o, Claude 3.5, or an open-source alternative like Llama 3) based on your accuracy needs, latency tolerance, and budget.
Pick a vector database (Pinecone, Weaviate, or Qdrant) to store and retrieve embeddings from your ingested documents.
Decide on an orchestration framework—LangChain, LlamaIndex, or a simple Python script—to chain retrieval, prompt construction, and response generation.

Write a data pipeline that fetches and parses your sources (PDFs, web pages, RSS feeds) and splits them into semantically meaningful chunks of 500–1,000 tokens.
Generate embeddings for each chunk using a model like text-embedding-3-small and upsert them into your vector database with relevant metadata (source, date, topic).
Implement incremental updates so your assistant stays current without re-ingesting the entire corpus every time.

Create a retrieval function that converts a user query into an embedding, performs a similarity search in your vector DB, and returns the top‑k most relevant chunks.
Construct a prompt template that injects the retrieved chunks as context and instructs the LLM to answer based only on that context (with a “no answer” fallback).
Add a reranking step (e.g., Cohere Rerank or a cross-encoder) to improve the relevance of the top
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