Design the graph schema
Define nodes, edges and attributes that capture the domain facts and the retrieval granularity you need for prompts.
01 / THE IDEA
Knowledge Graph + LLM in practice.
A knowledge-graph plus LLM assistant pairs structured graph data (entities, relations, features) with an LLM that consumes subgraphs or retrieved facts to answer queries. Important engineering topics include graph design, feature engineering for nodes/edges, retrieval strategies for LLM prompts, and evaluation that avoids leakage across connected components.
Real-world use case · Proposed extension
A proposed application is a retrieval-augmented assistant for internal documentation: retrieve a subgraph relevant to a user query, format facts for an LLM prompt, and return a source-linked answer with a human reviewer in the loop for trust and correction. This extension is not implemented by the supplied starter.
02 / THE BUILD
How to build a knowledge graph and llm assistant project
step by step.
Construct graph features
Compute structural features such as degree, clustering and PageRank to help rank or filter candidate facts.
Build a retrieval policy
Design how to select a subgraph or passages for the LLM prompt, including thresholds and component-based splitting to avoid leakage.
Evaluate end-to-end
Measure task success rate, attribution (source recall), and monitor hallucination cases via human review and targeted test queries.
03 / SOURCE SNAPSHOT
What the knowledge graph and llm assistant starter actually does.
The supplied starter loads NetworkX's Karate Club graph, computes per-node degree, clustering and PageRank, creates a pandas DataFrame of these features, derives a simple weighted percentile-based 'risk_or_class_score', and prints the top-ranked nodes. It does not implement any LLM integration, retrieval-augmented generation, training of message-passing models, or assistant logic.
SOURCE EXCERPTPython
features["risk_or_class_score"] = (
0.5 * features["degree"].rank(pct=True)
+ 0.3 * features["pagerank"].rank(pct=True)
+ 0.2 * features["clustering"].rank(pct=True)
)
print(PROJECT_NAME)Important limitation
The starter only computes basic structural features on the built-in Karate Club graph and a heuristic weighted rank. It contains no LLM calls, no retrieval pipeline, no real domain knowledge graph data, and no evaluation of an assistant; therefore it does not implement the titled knowledge-graph plus LLM assistant workflow.
04 / BEYOND THE CLASSROOM
Where a knowledge graph and llm assistant project could go next.
PROPOSED BUSINESS CONCEPT
Small QA assistant built on subgraph retrieval
A proposed extension: replace the toy graph with a domain knowledge graph, implement a retrieval policy that returns a subgraph or passages, and prompt an LLM to produce answers that cite source nodes and edges; surface these answers in an API with an approval step for human reviewers.
WHO IT COULD SERVEInternal documentation teams
FIRST MVPA minimal QA API that retrieves the top-k relevant nodes/edges for a query, constructs an LLM prompt with those facts, returns an answer plus source links, and requires a human reviewer before publishing.
KEY RISKThe assistant may hallucinate or cite irrelevant graph facts; keep a human reviewer in the loop and do not use the prototype for critical decisions or automated actions without verification.
This is a potential application pathway, not a delivered product. The supplied starter does not implement this service or validate it with real users.
05 / WHAT YOU TAKE AWAY
What a knowledge graph and llm assistant project puts on your resume.
- Compute and inspect common graph features such as degree, clustering coefficient and PageRank for node ranking.
- Design a simple weighted scoring rule to rank nodes and identify influential graph elements.
- Plan a retrieval-augmented workflow that selects subgraphs for LLM prompts and think through data leakage risks.
- Define evaluation metrics for an assistant: task success rate, source recall, and a manual auditing process for hallucinations.
Want to build this with mentors and a lab?
AI Engineer Course Networkers Home runs this topic inside its AI Engineer Course, with placement support and hands-on labs.
06 / QUESTIONS STUDENTS ASK
Knowledge Graph and LLM Assistant project FAQs.
- Is this suitable as a final year project for CSE students?
- Yes — it spans graph engineering, feature design and LLM integration, offering substantial scope for novel design, experiments and evaluation suitable for an advanced final year project.
- What technical skills and tools will I need?
- Familiarity with Python, NetworkX, pandas and prompt engineering is essential; knowledge of graph algorithms, embeddings, retrieval methods and LLM APIs will help implement robust solutions.
- How should I evaluate progress and how long will it take?
- Measure retrieval accuracy, response relevance, and leakage across graph components; expect 3–6 months to design, implement experiments and iterate on evaluation for a solid submission.