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Portfolio

AI/ML Engineering · Capstone

Sample: RAG Course Assistant

Students asked the same questions about course material again and again, and answers were buried in hours of recordings.

Built bySample Student IsharaSample Student Ravindu
Outcome

Answers grounded in course notes with citations, evaluated at 87% faithfulness.

01

Problem & context

Sample content. Course knowledge was spread across slides, notebooks and video transcripts.

02

What was built

A retrieval-augmented assistant that answers questions from course material and cites its sources.

03

Approach

Chunked transcripts and notes, embedded them into a vector store, and added an evaluation loop with Opik to track faithfulness and relevance.

04

Results & evaluation

Faithfulness 0.87, answer relevance 0.91 on a 120-question eval set.

Read the breakdown

Project breakdownAI/ML Engineering

Sample: How we built a RAG assistant that cites its sources

A walkthrough of the problem, architecture, key decisions and limitations behind a course-notes RAG assistant from Beyond the Notebook.

8 Oct 2026 · 6 min read
Verified score
94/100
#1 in cohort · Capstone
PathwayAI/ML Engineering
BootcampAI Engineer Essentials: LLMOps & Agentic Systems
CohortCohort 03 · 2026
Type · DifficultyCapstone · Advanced
Tools & skills
  • Python
  • LangChain
  • RAG
  • FastAPI
  • Opik
Links Source on GitHub

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