Build an AI-Powered Document Summarizer & Q&A System

 

Documents contain a huge amount of valuable information, but finding the right information inside hundreds or thousands of files can be difficult. Generative AI has changed this by making it possible to build systems that can read documents, summarize them, search their content, and answer questions using the information they contain.

Build an AI-Powered Document Summarizer & Q&A System is an intermediate-level course from Board Infinity on Coursera that focuses on building exactly this type of application. The course teaches a complete Retrieval-Augmented Generation (RAG) workflow, starting with document ingestion and text processing and progressing toward embeddings, vector search, summarization, Q&A, evaluation, deployment, and monitoring.

What Is an AI-Powered Document Assistant?

An AI document assistant is a system that allows users to interact with their documents using natural language.

Instead of manually searching through a large PDF, a user can ask questions such as:

"What are the main conclusions of this report?"

"Summarize this document."

"What does the policy say about refunds?"

The AI system searches the relevant document content and generates a response based on the retrieved information.

The overall architecture can be represented as:

Documents

↓

Text Extraction

↓

Cleaning & Chunking

↓

Embeddings

↓

Vector Database

↓

Semantic Search

↓

LLM

↓

Summary / Answer

The course develops this architecture through an evolving AI Knowledge Assistant project.

Understanding RAG

One of the most important concepts covered in the course is Retrieval-Augmented Generation, or RAG.

RAG combines two major capabilities:

Information Retrieval + Generative AI

Instead of asking a language model to answer a question only from its existing knowledge, the application first searches a collection of documents for relevant information.

That information is then provided to the language model as context.

The basic flow is:

User Question

↓

Retrieve Relevant Information

↓

Provide Context to LLM

↓

Generate Answer

This approach is particularly useful for private, organizational, technical, or frequently changing information.

Document Ingestion

Before an AI system can answer questions about documents, the documents need to be processed.

This is called document ingestion.

The course covers ingestion from different formats, including PDF, DOCX, scanned documents, tables, and HTML content.

This is important because real-world document collections are rarely uniform.

A company knowledge base might contain:

• PDF reports
• Word documents
• Web pages
• Scanned documents
• Tables
• Manuals
• Research papers
• Policy documents

An effective AI system needs to process these different sources.

Text Cleaning and Normalization

Extracted document text may contain formatting problems, unnecessary whitespace, broken characters, or other unwanted information.

Cleaning and normalization prepare the extracted content for later processing.

The course includes dedicated material on text cleaning and normalization.

Clean text is important because poor preprocessing can negatively affect:

• Chunking
• Embeddings
• Retrieval
• Summarization
• Question answering

Tokenization

Large language models process text through tokens.

A token may represent a complete word, part of a word, punctuation, or another text unit.

Understanding tokenization is important because LLMs have limited context windows.

The course covers tokens, tokenization, and context windows as part of its document-processing foundation.

Context Windows

An LLM cannot process an unlimited amount of text in a single request.

Large documents can therefore create a problem.

If a document is too large to fit into the model's context window, the system needs to divide and process it intelligently.

This is one reason why chunking, retrieval, and long-document summarization techniques are important.

Document Chunking

Chunking divides a large document into smaller pieces.

Instead of treating an entire document as one huge block, the system creates manageable sections.

For example:

Large Document

↓

Chunk 1

Chunk 2

Chunk 3

Chunk 4

These chunks can then be converted into embeddings and stored for retrieval.

The course covers multiple chunking strategies, including structure-aware hierarchical chunking.

Structure-Aware Chunking

Documents often contain meaningful structures such as:

• Headings
• Sections
• Paragraphs
• Chapters
• Tables
• Subsections

Structure-aware chunking attempts to preserve these relationships.

This can improve retrieval because the system is less likely to separate important contextual information.

Embeddings

Embeddings convert text into numerical representations.

A text passage is represented as a vector in a mathematical space.

Texts with similar meanings can have embeddings that are close to each other.

This allows the system to perform semantic search rather than relying only on exact keyword matches.

The course introduces embedding concepts and uses Sentence Transformers for embedding generation.

Semantic Search

Traditional search often depends heavily on matching words.

Semantic search focuses more on meaning.

For example, a user could ask:

"How can I terminate my membership?"

A document might say:

"Customers may cancel their subscription at any time."

The wording is different, but the meaning is closely related.

Embeddings allow the system to identify this semantic relationship.

Vector Databases

Once document chunks have been converted into embeddings, they need to be stored and searched efficiently.

This is the role of a vector database.

The course introduces vector databases such as Chroma and FAISS.

A vector database allows the application to search for document chunks that are mathematically similar to the user's question.

Similarity Search

When the user asks a question, the question can also be converted into an embedding.

The system compares the question embedding with document embeddings and retrieves the most relevant chunks.

The process becomes:

Question

↓

Query Embedding

↓

Similarity Search

↓

Top-K Relevant Chunks

↓

LLM

This is one of the fundamental mechanisms behind a RAG application.

Metadata Filtering

Semantic similarity is useful, but metadata can provide additional control.

Documents can contain information such as:

• File name
• Date
• Category
• Author
• Department
• Document type

Metadata filtering can restrict retrieval to appropriate documents.

The course includes metadata filtering and hybrid retrieval.

Hybrid Retrieval

A strong retrieval system can combine different approaches.

For example:

Dense Semantic Retrieval + Keyword Retrieval

The course introduces hybrid retrieval using techniques such as BM25 and Reciprocal Rank Fusion (RRF).

This can improve retrieval when exact terminology and semantic meaning are both important.

Document Summarization

One of the main applications of the project is summarization.

A summarizer can reduce a long document into a shorter representation while preserving important information.

The course covers both:

Extractive Summarization

Important sentences or passages are selected from the original document.

Abstractive Summarization

The system generates a new summary that expresses the important ideas in its own words.

The course includes both approaches.

Long-Document Summarization

Large documents can exceed an LLM's context window.

The course therefore introduces approaches such as:

Map-Reduce

and

Refine

for processing long documents.

Map-Reduce Approach

The document is divided into sections.

Each section is summarized independently.

The individual summaries are then combined into a final summary.

Refine Approach

An initial summary is generated and progressively refined as additional sections are processed.

These techniques make it possible to work with documents that are much larger than a model's normal context window.

RAG-Based Question Answering

The next step is allowing users to ask questions about the document collection.

The process can be represented as:

Question

↓

Query Processing

↓

Embedding

↓

Retrieval

↓

Relevant Document Chunks

↓

LLM

↓

Answer

This creates a conversational interface for interacting with a document collection.

Grounded Answers

A major concern with generative AI is hallucination.

A language model may generate an answer that sounds convincing but is not supported by the source material.

A RAG system attempts to ground the answer in retrieved information.

The course specifically covers grounded answers with citations.

Citations

Citations make AI-generated answers easier to verify.

Instead of simply returning an answer, the system can indicate the document or source information that supports the response.

This is particularly important for:

• Business documents
• Research
• Technical documentation
• Policies
• Legal information
• Internal company knowledge

Citations improve transparency and allow users to check the original information.

Reranking

Initial retrieval may produce several relevant document chunks, but they may not all be equally useful.

Reranking provides another stage of relevance assessment.

The retrieved results can be reordered so that the most useful information is presented to the LLM first.

The course covers cross-encoder reranking as part of its retrieval optimization topics.

Context Compression

Providing too much information to an LLM can be inefficient.

Context compression attempts to remove unnecessary information while preserving the content that matters for the question.

This can help improve both efficiency and answer quality.

Conversational Memory

A basic Q&A system treats each question independently.

A conversational system can maintain relevant information from previous questions.

For example:

User: What is the company's leave policy?

AI: The policy provides...

User: Does it apply to new employees?

The second question depends on the previous context.

The course introduces conversational memory and follow-up questions.

Agentic RAG

The course also introduces Agentic RAG and tool use.

Traditional RAG usually follows a predefined retrieval process.

Agentic RAG can provide an AI system with greater flexibility in deciding how to retrieve information or which tools to use.

This represents an important step toward more advanced AI applications.

Open-Source Models

The course also explores alternatives to using only commercial language models.

It covers serving open-source models locally with Ollama and Hugging Face, along with quantization and replacing models inside the RAG pipeline.

This is useful for learners interested in:

• Local AI
• Privacy
• Cost control
• Model customization
• Self-hosted applications

API Development

A real-world AI application generally needs an interface through which other software can communicate with it.

The course therefore moves toward deploying the document assistant as an API.

This allows other applications to interact with the AI system programmatically.

Chat Dashboard

The project also progresses toward an interactive chat interface.

Users can interact with the document assistant through a conversational dashboard rather than manually running individual pieces of code.

According to the course page, the final outcome is a deployed, end-to-end GenAI application with an API and chat dashboard.

Monitoring and Observability

An AI application needs to be monitored after deployment.

Important information can include:

• Query latency
• Usage
• Cost
• Errors
• Retrieval quality
• Answer quality
• Quality changes over time

The course covers monitoring, logging, observability, and tracing with LangSmith.

RAG Evaluation

Building a RAG system is not enough.

It also needs to be evaluated.

Important questions include:

Did the system retrieve the correct information?

Was the answer relevant?

Was the answer supported by the retrieved context?

Did the model hallucinate?

The course introduces RAG evaluation metrics and automated evaluation with RAGAS.

Reducing Hallucinations

Hallucinations are one of the biggest challenges in generative AI.

A reliable document assistant should ideally:

• Use retrieved evidence
• Provide citations
• Avoid unsupported claims
• Indicate when information is unavailable
• Be evaluated regularly

The course includes techniques for detecting and reducing hallucinations and improving answer quality iteratively.

Production Deployment

The course goes beyond a basic notebook demonstration.

It covers:

• API deployment
• Interactive chat
• Monitoring
• Logging
• Evaluation
• Cost tracking
• Quality monitoring
• Docker

Docker is listed among the tools associated with the course.

This production-oriented approach makes the project more useful as a portfolio project.

Course Structure

The course currently contains 5 modules, is classified as intermediate level, and is designed around a flexible schedule. Coursera currently lists it as recently updated in July 2026, with 9 assignments.

Module 1 — Foundations, Document Ingestion & Text Processing

This module introduces LLM and RAG fundamentals, environment setup, document ingestion, cleaning, chunking, and tokenization.

Module 2 — Embeddings, Vector Search & Summarization

This section focuses on embeddings, Sentence Transformers, vector databases, similarity search, metadata filtering, and document summarization.

Module 3 — Retrieval-Augmented Generation & Q&A

The course develops the RAG Q&A pipeline and explores retrieval, summarization, metadata, and controllable summaries.

Module 4 — Optimization, Deployment & Best Practices

This module focuses on production retrieval, grounded answers, citations, conversational memory, hybrid search, and cross-encoder reranking.

Module 5 — Deployment, Monitoring & Best Practices

The final module covers query transformation, Agentic RAG, evaluation, RAGAS, hallucination reduction, monitoring, and improving answer quality.

Skills You Can Develop

The course covers a wide range of modern AI application skills, including:

• Retrieval-Augmented Generation
• Large Language Models
• Generative AI
• Embeddings
• Vector databases
• Prompt engineering
• Document processing
• Unstructured data
• Text mining
• Model evaluation
• API design
• Python
• Model deployment
• Docker
• Token optimization

Who Should Take This Course?

Python Developers

Developers with Python knowledge can use this course to move into LLM and RAG application development.

AI Engineers

It provides practical exposure to building and deploying AI-powered knowledge systems.

Machine Learning Engineers

ML engineers can strengthen their knowledge of retrieval systems, embeddings, evaluation, and production GenAI.

Data Scientists

Data scientists working with unstructured data can benefit from learning how documents can be transformed into searchable knowledge bases.

Generative AI Learners

Anyone interested in building practical LLM applications can benefit from the end-to-end project.

Prerequisites

Because the course is classified as intermediate, it is better suited to learners who already have some technical experience.

A learner will benefit from familiarity with:

• Python
• Basic programming
• Machine-learning concepts
• APIs
• Basic NLP or LLM concepts

It is not the ideal first course for someone who has never programmed before.

Strengths of the Course

Complete RAG Workflow

The course covers the complete journey from document ingestion to deployment.

Practical Project

The single evolving AI Knowledge Assistant keeps the learning connected rather than presenting unrelated examples.

Modern Retrieval Techniques

The course includes semantic retrieval, hybrid search, reranking, metadata filtering, and query transformation.

Summarization + Q&A

Learners get experience with both major document-AI use cases.

Evaluation

The inclusion of RAG evaluation and RAGAS is particularly valuable because building a working chatbot does not necessarily mean building a reliable chatbot.

Production Focus

Deployment, monitoring, observability, cost tracking, and quality monitoring make the course more relevant to real applications.

Limitations

The course covers a large number of topics in a relatively compact format.

Therefore, learners should not expect deep specialization in every technology.

For example, someone who wants advanced expertise in:

• LLM fine-tuning
• Multimodal RAG
• GraphRAG
• Advanced agent orchestration
• MLOps
• Cloud architecture

will need additional resources.

The course itself also notes that its RAG approach does not fully cover some broader areas of the modern AI-agent ecosystem.

Portfolio Value

One of the strongest aspects of this course is its project-oriented structure.

By the end, learners can have a project demonstrating:

Document Processing

→ Embeddings

→ Vector Search

→ RAG

→ Summarization

→ Q&A

→ Citations

→ Evaluation

→ API

→ Chat Dashboard

→ Deployment

Coursera specifically states that the resulting application is intended to be portfolio-ready and includes guidance for documenting the project on GitHub.

Career Relevance

The skills covered can be useful for roles such as:

• Generative AI Developer
• AI Engineer
• Machine Learning Engineer
• LLM Application Developer
• RAG Engineer
• Python AI Developer
• NLP Engineer

The course is particularly relevant for developers who want to move from using AI tools to building AI applications.

Learning Roadmap

A useful progression after this course could be:

Python

↓

Machine Learning

↓

NLP

↓

LLMs

↓

Prompt Engineering

↓

Embeddings

↓

Vector Databases

↓

RAG

↓

Advanced Retrieval

↓

Agentic AI

↓

Multimodal AI

↓

MLOps & Deployment

This provides a strong path toward modern Generative AI engineering.

Join Now: Build an AI-Powered Document Summarizer & Q&A System

Final Verdict

Build an AI-Powered Document Summarizer & Q&A System is a strong intermediate course for anyone who wants practical experience building RAG-based AI applications.

Its biggest strength is that it does not stop at explaining what RAG is. It takes learners through the complete workflow: document ingestion, cleaning, chunking, embeddings, vector databases, retrieval, summarization, question answering, citations, memory, reranking, evaluation, deployment, and monitoring.