1. Introduction

Retrieval-Augmented Generation (RAG) is powerful, yet teams often struggle to keep answers trustworthy once systems hit production scale. LangChain RAG combines chain-based orchestration with flexible retrieval; however, without LLM observability you can’t see why an answer drifts or a chunk gets missed. Therefore, this guide walks through three incremental upgrades—recursive, semantic, and Chain-of-Thought retrieval—while continuously measuring quality in Future AGI so you can ship confidently.

Follow along with our comprehensive cookbook for a hands-on experience: https://docs.futureagi.com/cookbook/cookbook5/How-to-build-and-incrementally-improve-RAG-applications-in-Langchain

2. Why LangChain RAG Needs Observability

Although LangChain makes chaining easy, consequently each component hides failure points: embedding drift, chunk overlap, prompt leakage and more. Moreover, RAG mistakes are subtle; an answer may look fluent yet cite the wrong source. LLM observability surfaces those blind spots by tracing every span, scoring context relevance and grounding each generation. As a result, teams debug faster and iterate with data rather than gut feel.

3. Tools for a Robust LangChain RAG Stack

For a production-ready LangChain RAG workflow we use:

Installing dependencies

pip install langchain-core langchain-community langchain-experimental
openai chromadb beautifulsoup4 futureagi-sdk

Image 1: A sample dashboard view in FutureAGI showing experiment results and key metrics for your RAG application.

Step 1 – Baseline LangChain RAG with Recursive Splitter

Setting up the dataset

import pandas as pd
dataset = pd.read_csv("Ragdata.csv")

Our CSV contains Query_Text, Target_Context and Category. Consequently, each query gets matched against Wikipedia pages for Transformer, BERT, and GPT.

Loading pages and splitting recursively

from langchain_community.document_loaders import WebBaseLoader

from langchain.text_splitter import RecursiveCharacterTextSplitter

from langchain_community.vectorstores import Chroma

from langchain.chat_models import ChatOpenAI

urls = [

    "https://en.wikipedia.org/wiki/Attention_Is_All_You_Need",

    "https://en.wikipedia.org/wiki/BERT_(language_model)",

    "https://en.wikipedia.org/wiki/Generative_pre-trained_transformer"

]

docs = []

for url in urls:

    docs.extend(WebBaseLoader(url).load())

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

chunks  = splitter.split_documents(docs)

vectorstore = Chroma.from_documents(chunks, embedding=embeddings,

                                    persist_directory="chroma_db")

retriever = vectorstore.as_retriever()

llm       = ChatOpenAI(model="gpt-4o-mini")

Running the baseline chain

def openai_llm(question, context):

    prompt = f"Question: {question}\n\nContext:\n{context}"

    return llm.invoke([{'role': 'user', 'content': prompt}]).content

def rag_chain(question):

    docs = retriever.invoke(question)

    context = "\n\n".join(d.page_content for d in docs)

    return openai_llm(question, context)

Meanwhile, the Future AGI instrumentor auto-traces every call, so you’ll later compare metrics across versions.

Evaluating the Baseline

Because evaluation drives improvement, we score three axes—Context Relevance, Context Retrieval and Groundedness—using Future AGI:

from fi.evals import ContextRelevance, ContextRetrieval, Groundedness

from fi.evals import TestCase

def evaluate_row(row):

    test = TestCase(

        input=row['Query_Text'],

        context=row['context'],

        response=row['response']

    )

    return evaluator.evaluate(

        eval_templates=[ContextRelevance, ContextRetrieval, Groundedness],

        inputs=[test]

    )

In contrast to eyeballing answers, these metrics quantify where retrieval fails.

Step 2 – Boost Recall with Semantic Chunking

Although recursive splitting is simple, however it may cut sentences mid-thought. SemanticChunker clusters by meaning, therefore recall often rises.

from langchain_experimental.text_splitter import SemanticChunker

s_chunker   = SemanticChunker(embeddings, breakpoint_threshold_type="percentile")

sem_docs    = s_chunker.create_documents([d.page_content for d in docs])

vectorstore = Chroma.from_documents(sem_docs, embedding=embeddings,

                                    persist_directory="chroma_db")

retriever   = vectorstore.as_retriever()

Early tests showed Context Retrieval improving from 0.80 ➜ 0.86, for instance.

Step 3 – Enhance Groundedness via Chain-of-Thought Retrieval

Complex questions often need multiple focused passages. Consequently, we generate sub-questions, gather context for each, then answer holistically.

from langchain_core.prompts  import PromptTemplate

from langchain_core.runnables import RunnableLambda, RunnablePassthrough

subq_prompt = PromptTemplate.from_template(

    "Break down this question into 2-3 SUBQ bullet points.\nQuestion: {input}"

)

def parse_subqs(text):

    return [line.split("SUBQ:")[1].strip()

            for line in text.content.split("\n") if "SUBQ:" in line]

subq_chain = subq_prompt | llm | RunnableLambda(parse_subqs)

qa_prompt = PromptTemplate.from_template(

    "Answer using ALL context.\nCONTEXTS:\n{contexts}\n\nQuestion: {input}\nAnswer:"

)

full_chain = (

    RunnablePassthrough.assign(subqs=lambda x: subq_chain.invoke(x["input"]))

    .assign(contexts=lambda x: "\n\n".join(

        doc.page_content

        for q in x["subqs"]

        for doc in retriever.invoke(q)

    ))

    .assign(answer=qa_prompt | llm)

)

As a result, Groundedness climbed to 0.31, the best of the three methods.

5. Evaluating Each LangChain RAG Approach

Image 2: Average of common columns across data-frames

5.1 Metrics at a Glance

5.2 Key Takeaways & Next Steps

1. Chain-of-Thought dominates retrieval and grounding, therefore it’s ideal for complex queries.

2. Semantic chunking balances speed and accuracy, meanwhile costing fewer tokens.

3. Use recursive splitting only when latency outweighs precision. Nevertheless, always track scores to avoid silent regressions.

6. Best Practices for Production-Grade LangChain RAG

• Cache frequent sub-questions; consequently, you slash token spend.

• Tune chunk size and overlap on real data—start at 1000/200; iterate.

• Monitor drift: embed new docs weekly, otherwise recall decays.

• Alert on grounding scores below a threshold, so bad answers never hit users.

Future Improvements

Moreover, consider hybrid strategies: semantic chunking first, then Chain-of-Thought only when the query exceeds a complexity heuristic. Similarly, explore task-specific embedding models for niche domains.

Conclusion

Ultimately, building with LangChain RAG is straightforward; sustaining accuracy is not. Therefore, pair every retrieval tweak with LLM observability in Future AGI. As a result, you’ll iterate quickly, catch silent failures early, and deliver grounded answers your users trust.

Ready to level-up your LangChain RAG pipeline? Start instrumenting with LLM observability today and watch your Retrieval-Augmented Generation accuracy soar—sign up for Future AGI’s free trial now!