TravelMate
An AI-powered travel assistant combining retrieval over travel knowledge with generative planning to answer destination and itinerary questions.
- Year
- 2025
- Engagement
- R&D — retrieval-augmented assistant
- Focus
- Generative AI, Full Stack, AI/ML
- Status
- Open source
01 — The problem
Trip planning is a research task spread across a dozen tabs. A general chatbot answers confidently but without grounding, which is exactly the wrong failure mode when someone is about to book something.
02 — Our solution
A retrieval-augmented assistant: relevant travel content is retrieved first, and generation is conditioned on what was retrieved, so answers stay tied to actual source material rather than model recall.
How it works
The pipeline, step by step — from the first input to the final output.
- 01
Knowledge ingestion
Travel content is collected, chunked and embedded into a vector store.
- 02
Retrieval
A user question retrieves the most relevant passages.
- 03
Grounded generation
The language model answers conditioned on retrieved context.
- 04
Itinerary assembly
Multi-part answers are composed into a usable plan.
What we built
The outcomes
01
A complete RAG application delivered end to end
02
Grounded answers traceable to retrieved source content
Challenges we solved
Retrieval quality caps answer quality
Weak retrieval produces confident nonsense. Chunking and embedding choices mattered more than prompt wording.
Travel data goes stale
Prices and schedules change, so the assistant is framed around planning guidance rather than live booking facts.
Technology stack
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