Testing location data with edge cases, not just happy paths
An address in a well mapped city center tells you almost nothing about how your system handles a rural route, a disputed border, or a query near the poles. Test the hard cases deliberately.
What "high precision" should mean for a geocoded result
The phrase "high precision" gets used loosely enough in casual conversation that it is worth being deliberate about what it should actually mean in the context of a geocoding response, because a vague understanding of the term leads directly to vague, poorly calibrated logic built on top of it.
High precision should mean something specific about the level of the match, not merely that an answer came back, and not merely that the answer came back quickly or without an error. In a well designed geocoding response, this is exactly what the precision field communicates directly: a value like house indicates the match resolved to a specific building or point, which is meaningfully different from street, indicating the match only resolved to a road segment, postcode, indicating resolution only to a postal area, or city, indicating a locality-level match. Each of these is a legitimate, honestly reported outcome. Only one of them should reasonably be called high precision in the sense that matters for use cases needing an exact location.
Confidence is a separate axis entirely and should not be conflated with precision when deciding what "high precision" means for your purposes. A house level match with low confidence is not actually a high precision result in any meaningful sense, since the low confidence signals real doubt about whether that specific building level match is correct. A genuinely high precision result, for practical purposes, should be understood as requiring both a fine-grained precision level and a confidence score comfortably above whatever threshold your own testing has shown to be reliable for your use case, not either one alone.
This distinction matters most in exactly the situations where getting it wrong is expensive. Dispatching a service technician, routing a delivery to a specific address, or triggering a location-based action tied to a precise point all genuinely need both a fine precision level and solid confidence together. Regional analytics, rough personalization, or broad content targeting can reasonably work with far coarser precision and do not need to hold every result to a house-level standard at all.
Rather than adopting someone else's definition of high precision wholesale, define your own threshold explicitly, in terms of the specific precision values and confidence range you require for your particular use case, and apply it consistently rather than treating "high precision" as a vague, subjective label. Full detail on the exact values precision can take is in the forward and reverse geocoding documentation, which is the right starting point for defining a threshold specific to what your application actually needs.