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Exactly Who Does What

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Real Code. Real Transparency.

Here's an actual snippet from a project like yours — with comments showing what AI wrote vs. what our engineers added.

# AI generated the pipeline scaffold. Engineers tuned chunking strategy,
# added hybrid search, and built the evaluation harness.

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_pinecone import PineconeVectorStore
from langchain.chains import RetrievalQA

class ProductRAGPipeline:
    def __init__(self, index_name: str):
        self.embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
        self.vectorstore = PineconeVectorStore(index_name=index_name,
                                               embedding=self.embeddings)
        self.llm = ChatOpenAI(model="gpt-4o", temperature=0.1)
        self.splitter = RecursiveCharacterTextSplitter(
            chunk_size=1000, chunk_overlap=200,
            separators=["\n\n", "\n", ". ", " "]  # tuned by engineers
        )

    def query(self, question: str, k: int = 4) -> dict:
        retriever = self.vectorstore.as_retriever(
            search_type="mmr",         # Max Marginal Relevance — engineer decision
            search_kwargs={"k": k, "fetch_k": 20}
        )
        chain = RetrievalQA.from_chain_type(
            llm=self.llm, retriever=retriever,
            return_source_documents=True
        )
        result = chain.invoke({"query": question})
        return {
            "answer": result["result"],
            "sources": [doc.metadata for doc in result["source_documents"]],
            "confidence": self._score_confidence(result)  # custom logic
        }

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