✅Accuracy

The platform is designed for high accuracy and to rigorously avoid misinterpreting satellite data through a sophisticated, multi-layered approach:

• Orchestration and reasoning with LangChain: Under the hood, our system uses LangChain to coordinate various AI models and tools. This allows the assistant to effectively reason, plan its approach to your query and then use a specialized suite of capabilities on your behalf.

• Deterministic geospatial analysis tools: When you ask a question that requires data analysis (e.g., "What was the tree cover loss in this area?"), the AI instructs deterministic analysis and map rendering services to perform the geospatial calculations. These are pre-built, robust tools (not generative AI outputs) that:

  • Execute precise geospatial analysis over your regions of interest.

  • Generate visual outputs on demand.

  • Crucially, return deterministic, verifiable results that come directly from our trusted, quality-assured GFW and Land & Carbon Lab data sources, ensuring the assistant's factual outputs are always grounded in reliable, objective data.

• Grounded generative insights with Retrieval Augmented Generation (RAG): While the core geospatial analysis is deterministic, the conversational layer and "generative insights" are powered by large language models. To ensure these LLMs provide answers that reflect real expertise and are factually correct (and don't "hallucinate"), the assistant is grounded using Retrieval Augmented Generation (RAG). This process works by:

  • Consulting a curated knowledge base: Instead of relying solely on its general training, the LLM first consults our specific, curated knowledge base. This includes up-to-date layer descriptions, comprehensive metadata, internal research papers and expert guidance from WRI and Land & Carbon Lab.

  • Providing context to the LLM: Relevant information retrieved from this knowledge base is provided as context to the LLM alongside your query. This ensures the assistant responds like a knowledgeable research assistant, pulling directly from our validated content.

• Continuous performance evaluation: We also run performance evaluations on the assistant. This ongoing testing ensures the tool maintains a high success rate when executing user queries and consistently delivers accurate and reliable results.

While Global Nature Watch's AI assistant is designed to generate high-quality, reliable insights by drawing on trusted geospatial data and carefully structured methodologies, it is still improving and may occasionally produce inaccurate responses, especially when interpreting quantitative results such as graphs or charts. The underlying data and map layers themselves are deterministic (not generated by AI) but large language models are used to explain and summarize those results, which can sometimes introduce errors.

If you notice something that seems incorrect, please click the thumbs down button to provide feedback. Your input helps us continue improving the tool's accuracy and usefulness.