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Forensic 10-K Audit
Given a company 10-K filing, implement a RAG pipeline that extracts risk factors and supports citations. Your solution must return JSON with section, summary, and quote. Evaluate retrieval quality and latency.
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
def build_rag(file_path: str):
loader = PyPDFLoader(file_path)
docs = loader.load_and_split()
embeddings = OpenAIEmbeddings()
return Chroma.from_documents(docs, embeddings)Real-World Sandboxes
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Interview-Grade Curriculum
From RAG optimization to Multi-Token Prediction, master the concepts that high-paying AI roles actually test for.
Challenge difficulty
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