Retrieval Augmented Generation (RAG) Systems Training in Vizag | Softenant Technologies
RAG systems connect a language model to a defined knowledge source so that answers can be based on retrieved passages instead of model memory alone. This course is about the complete retrieval pipeline: source preparation, chunking, embeddings, vector search, ranking, prompt assembly, answer citation and evaluation.
What makes this technology-specific
A RAG application is not simply a chatbot with uploaded files. Retrieval quality depends on how documents are cleaned, split, labelled and indexed; answer quality depends on what is retrieved, how much context is supplied and whether the model is instructed to abstain when evidence is missing. Learners examine these failure points and build systems that show their evidence rather than inventing answers.
RAG Systems syllabus
RAG architecture and use cases
Trace the path from a source document to ingestion, indexing, retrieval, prompt context, generated answer and user-facing citation.
Document preparation and chunking
Compare fixed-size, semantic and section-aware chunks; preserve metadata; remove noisy content; and decide what makes a chunk retrievable.
Embeddings and vector databases
Understand embeddings as semantic representations, similarity search, metadata filters, vector indexes and the role of a vector database.
Retrieval, ranking and context assembly
Work with top-k retrieval concepts, filters, reranking, context windows and prompts that require an evidence-bound answer.
Evaluation and failure analysis
Test retrieval relevance separately from answer correctness, investigate missed documents, duplicate chunks, stale sources and unsupported claims.
RAG application delivery
Design an internal-knowledge assistant with ingestion notes, source permissions, answer citations, feedback capture and maintenance steps.
Tools, concepts and working methods
Document ingestion
Cleaning, parsing, metadata and chunk strategy.
Embeddings
Semantic representations for similarity search.
Vector databases
Indexing, retrieval and filtered search concepts.
RAG evaluation
Retrieval relevance, groundedness and citations.
Portfolio projects with technical evidence
Policy-document assistant
Retrieve answers from a controlled policy library and show the source section used for each response.
Course knowledge search
Index course notes with metadata filters and compare keyword search with semantic retrieval.
RAG quality test suite
Create questions, expected evidence, failure labels and improvement notes for a knowledge assistant.
Career direction
RAG developer trainee, AI knowledge-base implementer, AI solutions trainee and LLM application developer. Training improves technical preparation; employment outcomes depend on portfolio quality, interviews, experience and available roles.
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Read Our Google ReviewsRAG Systems FAQs
What is the difference between RAG and fine-tuning?
RAG retrieves relevant external knowledge at query time. Fine-tuning changes model behaviour through training. They solve different problems and can sometimes be combined.
Why use a vector database?
It supports semantic retrieval of chunks that are similar in meaning to a query, often alongside metadata filters and ranking controls.