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Module 17 · Page 18 of 20 · 9 sections

End-to-End Project Design

Putting every piece together in one project: a RAG document assistant with its architecture, flow, tech stack and evaluation.

07RAG System Design10System Components Deep Dive14Evaluation and Testing16Real-world Case Studies
17.1

1. Introduction

  • End-to-end project design helps us apply all the concepts (data, models, deployment, monitoring, etc.) to solve a real-world problem.
  • It gives hands-on experience and improves system design, coding and problem solving skills.
  • In interviews, end-to-end projects are very important to showcase practical knowledge.
17.2

2. Project Example: RAG-based Document Assistant

Problem statement. Build a system that allows users to upload documents (PDF, DOCX), ask questions and get accurate answers using RAG (Retrieval Augmented Generation).

Key features

  1. 1Upload and process documents (PDF, DOCX, TXT).
  2. 2Store document embeddings in a vector database.
  3. 3Retrieve relevant context and generate accurate answers using an LLM.
  4. 4Provide chat interface with source references.
  5. 5Deploy the application with monitoring and feedback.
Related07RAG System Design
17.3

3. High Level Architecture

User InterfaceWeb app (Streamlit / React), upload documents, ask questions, view responses with sources
Backend APIFastAPI / Spring Boot, authentication, request handling, orchestration, logging
Document ProcessingExtract text (PDF, DOCX), chunking, cleaning, generate embeddings (SentenceTransformer)
Vector DatabaseStore embeddings (Qdrant / Pinecone / Weaviate), similarity search, metadata
LLM ServiceGenerate response (OpenAI / Llama / Ollama), use retrieved context, provide source references
Monitoring, logging and observability (Prometheus, Grafana, ELK)
17.4

4. Detailed Flow

  1. 1User uploads a document.
  2. 2Extract text and split into chunks.
  3. 3Generate embeddings for each chunk.
  4. 4Store embeddings in the vector database.
  5. 5User asks a question.
  6. 6Retrieve relevant chunks using similarity search.
  7. 7Pass retrieved context and the question to the LLM.
  8. 8Generate a response with source references.
  9. 9Log user interactions and feedback.
17.5

5. Tech Stack (Example)

LayerTools / technologies
FrontendStreamlit / React / Next.js
BackendFastAPI (Python) / Spring Boot (Java)
Document processingPyPDF2, Unstructured, LangChain
EmbeddingsSentenceTransformer (all-MiniLM-L6-v2)
Vector databaseQdrant / Pinecone / Chroma
LLMOpenAI / Llama / Ollama
MonitoringPrometheus, Grafana, ELK
DeploymentDocker, Kubernetes, AWS / GCP
17.6

6. Key Components

1
Document Ingestion
Extract and preprocess documents.
2
Text Chunking
Split text into smaller chunks.
3
Embedding Generation
Convert text to vector embeddings.
4
Vector Storage
Store and search embeddings.
5
Retrieval
Find relevant chunks for a user query.
6
Prompt Construction
Create a prompt with context.
7
LLM Inference
Generate the final answer.
8
Source Reference
Show document sources.
9
User Feedback
Collect feedback and improvement data.
10
Monitoring & Logging
Track usage, latency and errors.
17.7

7. Evaluation Metrics

1
Retrieval Quality
Precision@K, Recall@K.
2
Answer Relevance
Human or LLM-as-a-Judge.
3
Faithfulness
Check that the answer is grounded in the documents.
4
Response Latency
Time taken to generate an answer.
5
User Satisfaction
Feedback ratings.
6
Cost
API and infrastructure cost.
Related14Evaluation and Testing
17.8

8. Possible Enhancements

  1. 1Support multiple document formats (PDF, DOCX, PPT, etc.).
  2. 2Add authentication and role-based access.
  3. 3Implement hybrid search (keyword + vector).
  4. 4Use re-ranking models for better results.
  5. 5Support multi-modal documents (images, tables).
  6. 6Deploy on the cloud with auto scaling.
  7. 7Add an analytics dashboard for usage and feedback.
17.9

9. Key Takeaways

  • End-to-end projects help in practical learning.
  • Cover multiple concepts from data to deployment.
  • Improve problem solving and system design skills.
  • Useful for interviews and real-world applications.
  • Can be extended to different domains (finance, healthcare, education, etc.).