AI & Data
Generative AI & LLMs Challenges
Generative AI & LLMs challenges put you inside the work of building with large language models. You'll develop skills in prompt patterns, few-shot prompting, chain-of-thought, and LLM API integration, learning how these models behave before you scale them.
From there you'll handle the harder edges — RAG architectures, vector database basics, fine-tuning, and prompt versioning — putting LLM guardrails and LLM evaluation around every deployment the way AI teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
- CodeIntermediateNew
Multi-Agent Research Assistant for Biotech Patent Review
You receive 20 historical patent applications with the firm's own prior-art memos as ground truth. Design and build a 3-agent system: (a) Searcher — issues queries to a patent-s…
- Ai Agents
- Multi Agent Collaboration
- Agent Evaluation
AI Agents and LLM-Based Agents - CodeIntermediateNew
Build a LangGraph Multi-Agent Researcher
Design the four-agent topology with explicit message contracts. Implement each agent as a separate LLM call with role-specific system prompts, tool access (web search for resear…
- Multi Agent Orchestration
- Langgraph Or Crewai Workflows
- Tool Use
Multi-Agent Systems - AnalysisBeginnerNew
Cost-Optimize an Embedding Pipeline for a Customer Support Knowledge Base
You receive: (a) the current pipeline (full re-embed on any article change, OpenAI text-embedding-3-large, 3,072 dims) with one month of cost logs, (b) a sample of 5,000 article…
- Embedding Models
- Finops & Cost Optimization
- Change Detection
Vector Databases and Embeddings - ResearchSeniorNew
Multi-Tenant Vector Isolation for a B2B Knowledge Assistant
Build a small proof-of-concept in your chosen vector store (Pinecone or Qdrant — pick one and justify) that supports 10 simulated tenants with 1,000 vectors each. Implement the …
- Multi Tenant Isolation
- Vector Databases
- Threat Modeling
Vector Databases and Embeddings Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
LLM-Powered FAQ Chatbot for 40-Person SaaS Scale-up
You have access to TaskFlow's internal documentation, help articles, and a sample of 500 support tickets. Your task is to build a retrieval-augmented generation (RAG) pipeline: …
- Large Language Models
- RAG Architectures
- Information Retrieval
Text Analytics and Natural Language Processing - ResearchIntermediateNew
QLoRA Fine-Tune for a Customer-Support Domain Assistant
You receive 8,000 anonymized support ticket pairs (question -> agent response), the company's product documentation (around 600 pages), and a strong RAG baseline already running…
- Qlora
- Fine Tuning
- RAG Architectures
Fine-Tuning Large Language Models - PresentationIntermediateNew
Design a Hybrid Symbolic-Neural Agent for an Enterprise RAG Demo
Design a hybrid agent for a 'company-policy assistant' demo: a symbolic planner decomposes user goals into typed subtasks ('find policy', 'check applicability', 'compose answer'…
- Hybrid Ai
- Symbolic Planning
- RAG Architectures
Artificial Intelligence: Principles and Techniques - CodeIntermediateNew
Build a Tool-Calling Agent for an Internal Reporting Bot
You will implement the agent in either LangChain or LlamaIndex (your choice; defend it in the readme). Wire 4 tools: (1) read-only SQL on a sample warehouse, (2) a mocked BI met…
- Agent Orchestration
- Tool Calling
- Langchain Or Llamaindex
LLM Application Development - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- DesignIntermediateNew
Design a Continuous Eval Pipeline for an Enterprise RAG Product
Design (and partially build) a continuous-eval pipeline for a RAG system: (1) a structured eval set with at least 50 queries grouped by query class; (2) automated scoring (LLM-a…
- Continuous Evaluation
- LLM Evaluation
- RAG Architectures
AI Measurement and Evaluation - CodeIntermediateNew
Ship an MVP RAG Knowledge Assistant for a Climate-Tech Startup
As a 4-person team across a 6-week sprint, ship: (1) an ingestion pipeline for around 4,000 mixed PDFs and markdown files; (2) a vector store with documented chunking strategy; …
- RAG Architectures
- Software Engineering For Ai
- Vector Databases
AI Software Engineering Group Project - CodeIntermediateNew
Wire a Knowledge Graph into a Pharma RAG Assistant
You receive: 100 internal benchmark questions with reference answers; a 50,000-document anonymized RAG index; a curated drug-target-disease KG (~80,000 triples) loaded into a tr…
- Kg Grounded RAG
- Sparql
- Entity Linking
Knowledge Graphs and Semantic Web - DesignBeginnerNew
Build the PRD for an Internal RAG Knowledge Assistant
You receive: a description of the CS workflows (post-sale onboarding, escalation, renewal), an inventory of internal knowledge sources (Notion, Salesforce, Zendesk macros, 3 pro…
- Product Management
- RAG Architectures
- Evaluation Design
AI for Business and AI Product Management Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- PresentationBeginnerNew
Pitch an LLM Earnings-Call Analyst to an Equity Long-Short Team
Pick 3 publicly available US tech earnings-call transcripts (from a free source like sec.gov filings or company investor-relations pages) and build a retrieval-augmented LLM wor…
- Prompt Patterns
- RAG Architectures
- LLM Evaluation
AI and Quantitative Finance - CodeIntermediateNew
Build a Cross-Lingual Retrieval-Augmented QA System
Index around 5,000 internal-knowledge docs across the three languages using a multilingual embedding model (e.g., multilingual-e5 or BGE-M3). Build the retrieval-then-answer pip…
- RAG Architectures
- Cross Lingual Retrieval
- Multilingual Embeddings
Neural Networks for NLP - CodeIntermediateNew
Build a Vector-Search Backend for an Enterprise AI Knowledge Assistant
You receive a corpus of around 20,000 PDFs (mixed scanned and digital) totalling around 30 GB and a labeled retrieval set of 200 queries with human-judged ground-truth passages.…
- RAG Architectures
- Vector Database Basics
- Word Embeddings
Data Engineering and Big Data Systems
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.