Design a Hybrid Symbolic-Neural Agent for an Enterprise RAG Demo
Overview
What this challenge is about.
Design a Hybrid Symbolic-Neural Agent for an Enterprise RAG Demo. Advanced challenge in presentation. Communicating complex ideas to real audiences, earn a b...
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.
This is not a communication exercise. It is the work a professional does when they need to persuade a real audience. That distinction matters to every hiring manager who has seen candidates give class presentations and none who have communicated under real stakes.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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