Design a Hybrid Symbolic-Neural Agent for an Enterprise RAG Demo
Overview
What this challenge is about.
Build a hybrid symbolic-neural agent for a company-policy RAG demo and earn a verifiable certificate.
The scenario
The consultancy (around 50 staff, 40 enterprise clients) sells its workshops at around USD 35,000 per engagement; a sharp, opinionated demo that lands the symbolic-plus-neural argument is a known door-opener.
The Brief
What you'll do, and what you'll demonstrate.
Build and present a hybrid symbolic-neural enterprise assistant that beats a pure-LLM baseline on citation accuracy.
Earning criteria — what you'll demonstrate
- Design a hybrid symbolic-plus-neural architecture for a realistic task
- Implement a small planner and integrate it with a RAG back-end
- Measure citation accuracy as a deployable-LLM metric
- Present a methodology argument to a skeptical enterprise audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Artificial Intelligence: Principles and Techniques
Master · Ai Systems
Strong alignment
This challenge maps to Artificial Intelligence: Principles and Techniques at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Hybrid Ai
Apply hybrid ai to solve real industry problems and demonstrate production-level capability.
- Symbolic Planning
Apply symbolic planning to solve real industry problems and demonstrate production-level capability.
- Retrieval Augmented Generation
Apply retrieval augmented generation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Llm Evaluation
Apply llm evaluation to solve real industry problems and demonstrate production-level capability.
- Stakeholder Communication
Apply stakeholder communication to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Solutions Architect
Designing a hybrid architecture that respects both classical and neural AI is the architect's daily craft inside enterprise consultancies.
This challenge sharpens
- hybrid-ai
- symbolic-planning
- retrieval-augmented-generation
AI Engineer
Wiring up a planner plus RAG back-end into a working demo is the AI engineer's bread and butter at any AI-product company.
This challenge sharpens
- python
- retrieval-augmented-generation
- symbolic-planning
Prompt Engineer
Tuning the LLM layer for citation accuracy and instrumenting it for evaluation is precisely the prompt engineer's day-one work.
This challenge sharpens
- llm-evaluation
- retrieval-augmented-generation
- stakeholder-communication