Priya Raman

Priya teaches retrieval-augmented generation from the data up: parsing, chunking, retrieval and the evaluation that tells you whether any of it is working.

How Priya reviews work
Priya asks to see the retrieval results before the generated answers. Her reviews trace a wrong answer back to the stage where it went wrong — parsing, chunking, retrieval or generation.

About

Priya's view is that most RAG problems are retrieval problems in disguise. Her modules spend as much time on document parsing, chunking and metadata as they do on prompts, because answer quality rarely recovers from poor retrieval.

She guides learners through building ingestion pipelines, choosing between vector, keyword and hybrid search, and adding citations that can be checked against the source text. She also covers when RAG is the wrong tool and fine-tuning or a simpler search feature would serve better.

Priya encourages learners to keep a small labeled question set from day one, so every change to the pipeline can be measured rather than judged by a few hand-picked examples.

This is demo content. It does not describe a real person, employer, qualification or track record.

Teaches

Document parsing and ingestion pipelines
Chunking strategies and metadata design
Vector, keyword and hybrid retrieval with pgvector
Citations and grounded answer generation
Measuring retrieval quality with labeled queries

Field notes

fig. — choosing an approach

Fit of prompting, RAG and fine-tuning for common requirements
Requirement	Prompt	RAG	Fine-tune
Knowledge changes often	weak fit	strong fit	weak fit
Answers must cite sources	weak fit	strong fit	weak fit
Consistent style or format	partial fit	partial fit	strong fit

● strong fit · ◐ partial · ○ weak

RAGGuide9 min read

RAG vs. Fine-Tuning: Choosing the Right Approach

Retrieval and fine-tuning solve different problems. This guide breaks down what each one actually changes, where each tends to fail, and how to decide with evidence instead of instinct.

By Priya Raman · sample author

fig. — chunking with overlap

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c-042 · lines 4–8
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RAGExplainer9 min read

Chunking Strategies for Retrieval-Augmented Generation

How you split documents decides what your retriever can find. This explainer compares fixed-size, recursive, and structure-aware chunking, and shows how to evaluate the choice.

By Priya Raman · sample author

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Priya Raman

Priya teaches retrieval-augmented generation from the data up: parsing, chunking, retrieval and the evaluation that tells you whether any of it is working.

How Priya reviews work

Priya asks to see the retrieval results before the generated answers. Her reviews trace a wrong answer back to the stage where it went wrong — parsing, chunking, retrieval or generation.

Requirement

Prompt

RAG

Fine-tune

Knowledge changes often

Answers must cite sources

Consistent style or format