Berlin DataTalksClub Group

​During this session, we’ll take a practical look at the Analytics Engineer role: what it actually covers, how it fits into modern data teams, and which skills matter most.

​Rather than a step-by-step tutorial, this talk focuses on core concepts, real examples, and recurring patterns that define the work of Analytics Engineers today.

​We’ll cover:

• ​What the Analytics Engineer role really includes and how it complements data engineers, data scientists, and business teams
• ​Technical foundations that keep an AE effective: SQL, data modeling, testing, version control, and development workflows
• ​Soft skills that quietly shape impact: cross-team communication, stakeholder alignment, and systematic thinking

​LLMs are powerful, but they still hallucinate facts, especially when asked about entities, relationships, or claims that require up-to-date or structured knowledge.

​In this hands-on workshop, we'll explore how to use Wikidata as a grounding and fact-checking layer for LLMs to reduce hallucinations and make AI systems more reliable.

​We'll start with a short introduction to Wikidata and then set up the Wikidata MCP so an LLM can retrieve and verify facts rather than relying solely on its internal memory. This already provides a practical way to ground LLM outputs in verifiable data.

​From there, we’ll go beyond LLM-only approaches and build a small experimental fact-checking pipeline. The system combines semantic retrieval, LLM-based reranking, and natural language inference (NLI) to validate claims against evidence in a more controlled and interpretable way.

​This workshop focuses on evidence-driven verification pipelines that make LLM's reasoning steps explicit and easier to inspect, debug, and improve.

​What we'll cover:

• ​Wikidata as a structured source for factual verification
• ​Setting up and querying Wikidata using MCP
• ​Verifying claims with MCP + an LLM
• ​Moving beyond pure GenAI to evidence-based fact-checking
• ​Finding relevant Wikidata statements with semantic search
• ​Ranking candidate evidence with an LLM
• ​Verifying claims using an NLI model

​​What you'll leave with
By the end of the workshop, you'll be able to:

• Ground LLM outputs in structured data to reduce hallucinations
• ​Understand when LLM-only fact-checking is not enough
• ​Build a small, transparent fact-checking pipeline you can adapt to real projects