Generative AI Course in Guntur

Explain how Generative AI applications work.

• Artificial Intelligence, Generative AI and Machine Learning relationship
• Large Language Models, training and inference concepts
• Tokens, tokenisation, context windows, prompts and responses
• Model parameters, transformers and multimodal awareness
• Hallucinations, outdated knowledge, context limits and non-deterministic output

Write Python required for practical AI applications.

• Python setup, notebooks or IDE and virtual environments
• Variables, data types, strings and collections
• Conditions, loops, functions, modules and packages
• Exceptions, files, JSON and environment variables
• API requests, useful OOP and basic async awareness

Connect Python software to model services safely.

• HTTP request and response concepts
• API keys, authentication and secret handling
• System and user message concepts
• Response parsing, rate limits and token or cost awareness
• Retries, errors and provider-neutral application design

Design prompts as reusable application components.

• Instruction, context, input and output requirements
• Zero-shot, few-shot, examples, constraints and delimiters
• Structured prompts, templates and variables
• Prompt testing, iteration and failure analysis
• Prompt-injection awareness for applications

Turn model responses into validated application data.

• JSON outputs, schemas, fields and required properties
• Validation, parsing, retries and error handling
• Information extraction, classification and transformation
• Resume extraction and support-ticket classification
• Reliable downstream application use

Compare hosted, local and open-model options.

• Proprietary, open and open-weight model concepts
• Hugging Face Hub, transformers and tokenizer awareness
• Model selection, size, context and hardware trade-offs
• Hosted versus local inference
• Quantisation and fine-tuning awareness without deep model training

Build meaning-based retrieval features.

• Vector representations and semantic similarity
• Keyword versus semantic search
• Document, query and chunk embeddings
• Metadata, ranking and cosine-similarity awareness
• Semantic-search application lab

Store and retrieve embeddings for applications.

• Vector storage, collections, indexes and metadata
• Similarity search, filtering and retrieval
• Insert, update, delete and persistence concepts
• Choosing a vector store for an application
• FAISS, Chroma, Pinecone, Qdrant or equivalent batch-supported tools

Build grounded question-answering applications.

• Why RAG and when to use it
• Document loading, cleaning and chunking
• Embedding, indexing, retrieval and context assembly
• Grounded prompts, citations and source-aware responses
• Document question-answering application

Improve retrieval and answer quality.

• Chunking strategy, metadata filters and query rewriting
• Hybrid-search and reranking concepts
• Context compression and multi-document retrieval
• Retrieval diagnostics and grounded-answer quality
• Why RAG quality depends on retrieval quality

Use abstractions without hiding core behaviour.

• Framework purpose and abstraction trade-offs
• Prompt, model, parser and retrieval components
• Chains, runnables and state concepts
• LangChain-compatible patterns where appropriate
• Avoiding unnecessary framework complexity

Let models request controlled application actions.

• Tools, function schemas and arguments
• Validation, authorisation and controlled execution
• Search, calculator, database and internal-service tools
• Tool results and follow-up model responses
• Controlled tool-use assistant lab

Create bounded, stateful multi-step workflows.

• Agent versus workflow
• Planning, state, memory and step control
• LangGraph-style graphs, nodes and transitions
• Human approval, timeouts and bounded iteration
• Controlled multi-step agent workflow

Combine retrieval, routing and tools responsibly.

• Retrieval, tools and stateful workflows
• Routing questions to knowledge or actions
• Corrective retrieval and reflection awareness
• Multi-agent roles and orchestration trade-offs
• Agentic knowledge-assistant project

Test and protect AI applications.

• Test datasets, relevance, faithfulness and answer quality
• Prompt injection, data leakage and insecure tool use
• Input and output validation with access boundaries
• Privacy, copyright, bias and human oversight
• Treating an AI application as software that must be tested

Deliver and explain a complete AI application.

• Expose an AI workflow through FastAPI
• Environment configuration, logs and deployment basics
• Latency, reliability and cost awareness
• Build, test, secure and explain a capstone application
• Project presentation and interview preparation