the next generation of AI talent.
Solve realistic AI engineering scenarios, design the system, write the code, run tests, and get evaluated — then see how other engineers solved the same problem.
Customer Support RAG with Grounding Verification
Customer support agent needs to answer questions from docs with low latency, strict citation spans, and zero speculative hallucinations.
def retrieve_and_rerank(query: str):
hits = qdrant.search(query, limit=15)
pairs = [[query, h.payload["text"]] for h in hits]
scores = reranker.predict(pairs)
return filter_by_threshold(hits, scores)
AI engineering isn't learned by watching more tutorials.
You can understand RAG and still fail to debug a production retrieval pipeline. You can learn agents and still not know when an agent is actually the wrong solution. You can study system design and still struggle when the requirements are ambiguous.
CognitionCatalysts gives you realistic environments to build that skill.
Shift from passive consumption to the core engineering feedback loop.
Build real systems against unpredictable inputs, debug rate limits, balance retrieval latency against accuracy, and get structured AI evaluation to continually refine your engineering intuition.
Don't just answer the question.
Build the solution.
CognitionCatalysts challenges mirror the exact workflow of senior AI engineers solving production systems.
01 — Understand
Read a realistic business scenario.
Ingest enterprise requirements, latency budgets, data constraints, security boundaries, and ambiguous edge cases.
The CognitionCatalysts Challenge Workspace.
A comprehensive, dark-themed engineering workbench uniting problem context, architecture graphs, code execution, test harnesses, and automated AI evaluation.
Build a customer-support RAG system
A B2B SaaS platform handles 50,000 daily technical tickets. Documentation is split across Markdown wikis, OpenAPI specs, and resolved support threads. Customer queries often include exact code snippets and vague error logs.
- •Implement hybrid sparse + dense search with cross-encoder reranking
- •Enforce strict citation grounding with token-level span attribution
- •Filter out stale docs (< 90 days deprecation cycle)
- •Maintain end-to-end p95 latency under 1.8 seconds
- !Total budget: < $0.008 per answered ticket
- !Context window limit: 8k tokens to prevent latency spikes
- !Zero hallucination on billing & security policies
import os
from typing import List, Dict, Any
from langchain_core.documents import Document
from qdrant_client import QdrantClient
from sentence_transformers import CrossEncoder
class CustomerSupportRAG:
def __init__(self, collection_name: str = "saas_docs"):
self.qdrant = QdrantClient(host="localhost", port=6333)
self.reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
self.collection = collection_name
def retrieve_and_rerank(self, query: str, top_k: int = 5) -> List[Document]:
# 1. Hybrid search (Dense vector + BM25 sparse payload)
dense_results = self.qdrant.search(
collection_name=self.collection,
query_vector=self._embed_query(query),
limit=top_k * 3
)
# 2. Cross-Encoder reranking for precision
pairs = [[query, hit.payload["text"]] for hit in dense_results]
scores = self.reranker.predict(pairs)
# 3. Sort and truncate to optimal top_k
ranked = sorted(zip(dense_results, scores), key=lambda x: x[1], reverse=True)
return [Document(page_content=hit.payload["text"], metadata=hit.payload["meta"]) for hit, score in ranked[:top_k]]
def synthesize_grounded_response(self, query: str, docs: List[Document]) -> Dict[str, Any]:
context_str = self._format_cited_context(docs)
# Enforce strict grounding with structured refusal fallback
return self._generate_with_citations(query=query, context=context_str)Problems that don't have a “correct answer.”
Real AI engineering is full of trade-offs. Latency vs quality. Cost vs accuracy. Agents vs deterministic workflows. Retrieval vs context size. Evaluation coverage vs complexity.
Build a customer-support RAG system
Design retrieval, reranking, context construction and evaluation for a technical customer support assistant.
Dense vs Hybrid retrieval latency; Top-K context density vs LLM token cost; Cross-encoder precision vs response time.
Should this workflow be an agent?
A customer onboarding workflow has growing branching logic. Decide whether to introduce an autonomous agent or maintain deterministic DAG routing.
Autonomous flexibility vs Auditability & deterministic SLA; Tool execution cost vs Hardcoded branching maintenance.
Evaluate an LLM without ground truth
Design an automated evaluation strategy for an open-ended executive email drafting assistant where no canonical golden responses exist.
Single monolithic judge vs Specialized micro-judges; Evaluation prompt complexity vs Token cost; Reference-based vs Reference-free metrics.
Build a production AI deployment pipeline
Design canary rollouts, fallback routing, cost guardrails, and real-time observability for a mission-critical LLM gateway.
Gateway proxy latency vs Rich runtime validation; Multi-provider parity vs Provider-specific prompt tailoring; Shadowing cost vs Rollout safety.
Get feedback before you move on.
Every submission undergoes deep rubric evaluation across architecture, reliability, failure handling, and latency trade-offs. You receive concrete, structured feedback on what was strong and where to improve.
Challenge #42: Customer Support RAG System
- ✓Clear retrieval architecture separating BM25 and dense vector search
- ✓Good failure handling with explicit fallback on out-of-domain queries
- ✓Strong evaluation strategy with automated citation span checking
- →Explain reranking latency (cross-encoder adds 180ms to pipeline)
- →Consider context-window constraints when logs exceed 4,000 tokens
- →Add adversarial evaluation cases specifically targeting prompt injection
There isn't one right way to build AI.
See how engineers with different backgrounds approach the same problem. An AI Engineer prioritizes clean modularity, an FDE optimizes for client tenant isolation and latency, and an ML Engineer focuses on lexical query expansion.
- ✓Query expansion handles developer typos and esoteric acronyms flawlessly
- ✓Two-stage reranker prunes context before expensive LLM processing
- ✓Extremely clean modular pipeline with async concurrency
- ✓Strict zero-context refusal fallback prevents billing hallucinations
- ✓Sub-second latency (840ms) ideal for synchronous in-app widget
- ✓Guaranteed tenant document isolation via pre-filtered Qdrant payloads
“Adds ~120ms for query expansion step, but drastically reduces zero-recall failures on ambiguous customer tickets.”
Practice for the AI role you're becoming
Different AI roles require completely different engineering instincts. Select your target trajectory to see the realistic challenges you will solve.
AI Engineer
Build high-performance, grounded, production-grade applications that combine foundation models with custom retrieval, tools, and guardrails.
Build a Multi-Tenant Technical Support RAG with Dynamic RBAC
Design a retrieval pipeline that handles 50k queries/day across 800 enterprise tenants. Ensure document-level ACL filtering, hybrid search, and strict citation grounding without latency regression.