2. What skills will I gain from AI search and RAG training?

You can develop skills in semantic search, embeddings, vector databases, RAG pipelines, hybrid search, query expansion, reranking, and search evaluation. The course also covers APIs, conversational search, knowledge assistants, security, performance monitoring, and optimization.

3. How does AI search differ from traditional search?

Traditional search commonly relies on keyword or lexical matching, while AI-powered search can use semantic representations to identify content based on meaning and context. Combining retrieval with LLMs can also enable applications to generate responses grounded in retrieved information.

4. Does the course cover vector databases and RAG?

Yes. The curriculum covers embeddings, vector representations, vector databases, similarity search, retrieval-augmented generation, hybrid retrieval, reranking, and RAG quality improvement. Learners also explore how these components work together within AI search systems.

5. What practical projects are included in the AI search and RAG course?

Projects include building a document search application, creating an AI knowledge assistant, and deploying an AI search system. Learners also work with search APIs, evaluation methods, security controls, performance monitoring, and optimization techniques.

AI Search and RAG: Build AI-Powered Search Applications

Category Data Analytics & AI

Level Experts

Students 15 Already Enrolled

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(4.7)

AI search and RAG are transforming how applications find, retrieve, and present information from large collections of content. This course teaches the foundations of semantic search, vector retrieval, retrieval-augmented generation, and AI-powered search systems designed to provide relevant, grounded responses.

Learners explore document processing, chunking, metadata, embeddings, vector databases, similarity search, hybrid search, query expansion, reranking, and multi-query retrieval. The curriculum also covers connecting search systems with LLMs, building search APIs, conversational search, knowledge assistants, external data sources, and search context management.

The course also addresses evaluation, optimization, security, and production considerations. Learners assess relevance and retrieval quality, reduce hallucinations, monitor performance, manage latency and costs, protect search data, and prevent prompt injection while completing projects involving document search, knowledge assistants, and deployed AI search systems.

AI-powered search, semantic search, and RAG fundamentals
Document processing, chunking, metadata, and search indexing
Embeddings, vector databases, similarity search, and retrieval optimization
Hybrid search, query expansion, reranking, and multi-query retrieval
Search APIs, conversational search, knowledge assistants, and LLM integration
Search evaluation, security, performance, latency, and practical projects

Basic familiarity with artificial intelligence, search technologies, large language models, or software development is helpful but not mandatory. Some programming or API experience can be beneficial for learners working on application development and deployment topics.

This course is designed for AI developers, software engineers, ML professionals, data professionals, search specialists, and technology teams building knowledge-based applications. It is also suitable for learners interested in semantic search, RAG, conversational search, and AI-powered information retrieval.

1.1 Understanding AI-Powered Search
1.2 Traditional Search Versus AI Search
1.3 Semantic Search Fundamentals
1.4 Retrieval-Augmented Generation
1.5 AI Search Applications
1.6 Search System Capabilities and Limitations

2.1 Identifying Search Data Sources
2.2 Collecting and Processing Documents
2.3 Cleaning and Normalizing Content
2.4 Document Chunking Strategies
2.5 Managing Metadata and Attributes
2.6 Building Search Indexes

3.1 Understanding Text Embeddings
3.2 Creating Vector Representations
3.3 Choosing Embedding Models
3.4 Understanding Vector Databases
3.5 Performing Similarity Search
3.6 Optimizing Vector Retrieval

4.1 Designing AI Search Architecture
4.2 Implementing Semantic Search
4.3 Building Hybrid Search
4.4 Connecting Search with LLMs
4.5 Generating Grounded Search Responses
4.6 Handling Search Failures

5.1 Improving Search Query Understanding
5.2 Query Expansion Techniques
5.3 Metadata-Based Filtering
5.4 Reranking Search Results
5.5 Multi-Query Retrieval
5.6 Improving RAG Retrieval Quality

6.1 Building Search APIs
6.2 Creating AI Search Interfaces
6.3 Developing Knowledge Assistants
6.4 Implementing Conversational Search
6.5 Connecting External Data Sources
6.6 Managing Search Context

7.1 Measuring Search Relevance
7.2 Evaluating Retrieval Quality
7.3 Testing RAG Responses
7.4 Reducing Search Hallucinations
7.5 Monitoring Search Performance
7.6 Optimizing Search Costs and Latency

8.1 Protecting Search Data
8.2 Managing Access and Permissions
8.3 Preventing Prompt Injection
8.4 Building a Document Search Application
8.5 Creating an AI Knowledge Assistant
8.6 Deploying an AI Search System

An AI search and RAG course is suitable for AI developers, software engineers, ML professionals, data specialists, search professionals, and technology teams. It can also benefit learners who want to build intelligent search and knowledge-based applications.

AI Search and RAG: Build AI-Powered Search Applications

About AI Search and RAG: Build AI-Powered Search Applications

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