3.8. Frameworks — LangChain, LlamaIndex & Custom Pipelines

Generative AI & LLM Interview Guide for Top Roles (2025)

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🪄 Step 1: Intuition & Motivation

Core Idea: RAG (Retrieval-Augmented Generation) is a system — not a single model. It’s like a kitchen with multiple stations: data preparation, storage, retrieval, and cooking (generation). 🍳

Now, you can either:

Use a pre-designed kitchen (LangChain, LlamaIndex), where most tools are ready but fixed in layout.
Build your own kitchen (custom pipeline), giving you total control but requiring engineering effort.

Frameworks like LangChain and LlamaIndex make it easier to prototype, while custom pipelines shine in production — offering transparency, efficiency, and control.

Simple Analogy: Think of LangChain as a “no-code kitchen” — plug your ingredients (data), pick a recipe (prompt chain), and you get a meal (LLM response). But if you’re opening a 5-star restaurant (production-scale RAG), you’ll eventually want to design your own kitchen layout.

That’s the journey from LangChain → LlamaIndex → Custom Pipelines.

🌱 Step 2: Core Concept

Let’s explore the three major RAG framework approaches and how they differ in philosophy, flexibility, and control.

1️⃣ LangChain — The RAG Assembly Line

LangChain is the most popular RAG framework for building LLM-powered applications quickly.

It provides prebuilt “lego blocks” for the entire RAG pipeline:

DocumentLoader → Ingest PDFs, text files, websites.
TextSplitter → Chunk content into manageable sizes.
VectorStore → Store embeddings in FAISS, Pinecone, or Chroma.
RetrievalQA Chain → Combine retriever and LLM to answer queries.

Simplified flow:

User Query → Retriever → Retrieved Chunks → LLM → Final Answer

Example (conceptually):

“Where is the Eiffel Tower?” LangChain retrieves 2–3 chunks about Paris landmarks and feeds them to GPT, producing a coherent factual response.

Advantages:

Extremely quick setup.
Rich integrations (FAISS, Milvus, Chroma, OpenAI API).
Intuitive chaining and tool orchestration.

Challenges:

Debugging is hard — internal chains hide steps.
Performance overhead from abstraction layers.
Less control for optimization or caching at scale.

Perfect for prototyping, demos, and internal tools. Avoid for production-scale systems where transparency, latency, and debugging matter.

2️⃣ LlamaIndex — The Data-First Framework

LlamaIndex (formerly GPT Index) focuses on how data is represented and queried before generation.

Instead of simple chains, it builds data graphs — structured, queryable representations of knowledge. This allows:

Composable indexes: TreeIndex, ListIndex, SummaryIndex, VectorStoreIndex.
Structured querying: Convert complex questions into multi-hop or SQL-like queries.
Hybrid RAG: Mix retrieval, summarization, and graph traversal.

Example: Imagine your data is a company’s knowledge base: HR docs, financial reports, and chat logs. LlamaIndex builds a graph of these — connecting related entities — so queries like:

“What were last quarter’s revenue trends discussed by the finance team?” can navigate multiple sources intelligently.

Advantages:

Strong for structured queries and multi-hop reasoning.
Easier to customize data ingestion and retrieval logic.
Excellent for experimentation and smaller domain knowledge bases.

Challenges:

Still adds some abstraction overhead.
Scaling to 100M+ documents is non-trivial.
Requires internal understanding of “index graph” design.

Best for research, small-to-medium RAG systems, or semantic knowledge bases that need flexible, graph-based retrieval.

3️⃣ Custom Pipelines — The Engineer’s Playground

When you outgrow framework limitations, it’s time to build custom RAG pipelines using direct APIs.

Here’s what a typical production RAG architecture looks like:

  graph LR
A[User Query] --> B[Embed Query]
B --> C[Vector DB (FAISS / Milvus)]
C --> D[Retrieve Top-k Documents]
D --> E[Context Assembly & Summarization]
E --> F[LLM (OpenAI / Llama3 / Mistral)]
F --> G[Answer Generation + Grounding Check]

You control every stage:

Use custom embedding models (e.g., Instructor, E5).
Fine-tune retrieval parameters (nprobe, efSearch).
Manage caching, rate limits, and async pipelines.
Integrate telemetry, cost tracking, and A/B testing.

Advantages:

Transparent, fully controllable.
Optimized for speed, cost, and debugging.
Easier to integrate with enterprise systems (FastAPI, Redis, FAISS).

Challenges:

Requires engineering expertise.
More boilerplate (no plug-and-play).
Harder for newcomers to maintain.

Go custom when:

You need low latency and high throughput (production-grade).
You require debug visibility or custom logging.
You want hybrid RAG + tool use + evaluation pipelines.

📐 Step 3: Conceptual Comparison

Feature	LangChain	LlamaIndex	Custom Pipeline
Setup	🟢 Fast	🟡 Moderate	🔴 Manual
Flexibility	⚪ Moderate	🟢 High	🟢 Full
Debugging	🔴 Hidden	🟡 Partial	🟢 Transparent
Performance	⚪ Average	⚪ Average	🟢 Tunable
Scalability	⚪ Prototype scale	🟡 Medium	🟢 Enterprise scale
Best Use	Quick prototypes	Structured retrieval	Production systems

LangChain → abstraction for speed. LlamaIndex → structure for understanding. Custom RAG → control for production.

🧠 Step 4: Key Assumptions

Frameworks simplify RAG but abstract away control.
Complex pipelines (multi-hop, hybrid retrieval) need customization.
Production-grade RAG requires observability and cost management — which frameworks rarely provide.
Framework choice depends on the trade-off between speed of development and depth of control.

⚖️ Step 5: Strengths, Limitations & Trade-offs

✅ Strengths:

Frameworks accelerate learning and prototyping.
Enable modular RAG pipelines with minimal code.
Offer connectors to multiple storage and model providers.

⚠️ Limitations:

High abstraction overhead in LangChain.
Debugging complex chains is difficult.
LlamaIndex graph abstraction can confuse beginners.
Custom setups demand strong software engineering skills.

⚖️ Trade-offs:

Abstraction vs. Control: More abstraction means faster iteration but less transparency.
Convenience vs. Optimization: Frameworks simplify workflows but limit fine-tuning.
Development Speed vs. Scalability: Prototyping frameworks are quick but don’t scale without refactoring.

🚧 Step 6: Common Misunderstandings

🚨 Common Misunderstandings (Click to Expand)

“LangChain = RAG.” → No, it’s a framework for building RAG; you can build RAG without it.
“Frameworks are always slower.” → Not necessarily; small-scale LangChain setups can be fast.
“LlamaIndex is only for graphs.” → It supports traditional retrieval too, with extra structure.
“Custom pipelines take forever to build.” → Once modularized, they’re maintainable and scalable.

🧩 Step 7: Mini Summary

🧠 What You Learned: LangChain, LlamaIndex, and custom pipelines represent three tiers of RAG development — from easy prototyping to full-scale engineering.

⚙️ How It Works: LangChain builds fast chains, LlamaIndex structures data semantically, and custom pipelines give total transparency and control over retrieval, orchestration, and generation.

🎯 Why It Matters: Framework choice defines how fast you can prototype, how deep you can debug, and how well your RAG system scales in production — all critical discussion points in top tech interviews.

3.9. Serving RAG in Production 3.7. Evaluation and Diagnostics of RAG