AI product planning
AI vs Generative AI: Differences and Product Use Cases
By Haris Ahmed · Code Huddle · Product engineering guides
Artificial intelligence is the broader field. Generative AI is one part of it, focused on producing content. A product can use AI to classify, rank, or predict without generating text. Choose the technique around the decision users need to make and the evidence available to evaluate it.
How AI, machine learning, and generative AI relate
AI covers systems designed to carry out tasks associated with intelligence. Machine learning uses data to learn patterns rather than requiring every rule to be written explicitly. Generative AI produces content based on learned patterns and the input it receives.
These are overlapping categories, not three competing products. A property application might use a ranking system for discovery and a language model to explain the results.
| Need | Possible approach | Example evaluation |
|---|---|---|
| Assign a category | Classification | Correct and incorrect labels on representative examples |
| Order relevant results | Ranking or matching | Relevance judgments and user task completion |
| Estimate an outcome | Prediction | Error against observed outcomes |
| Draft or summarize content | Generative AI | Factual accuracy, completeness, and user review |
| Answer using private documents | Retrieval plus generation | Correct retrieval, source support, and access enforcement |
Start with the task and the available data
Write down the input, expected output, and consequence of an error. Then inspect the examples, labels, documents, or historical outcomes available. A short requirements review can reveal whether a rules-based implementation would meet the need before introducing model dependencies.
For example, a fixed eligibility policy may be clearer as application rules. Summarizing a long narrative may benefit from a language model. Neither choice should depend only on whether the feature can be marketed as AI.
Distinguish retrieval from model training
Retrieval supplies selected information to a model at request time. Fine-tuning changes a model using training examples. They solve different problems: adding reference documents does not automatically require training a new model.
For a knowledge feature, specify document permissions, source references, update frequency, and handling of unanswered questions. For a learned prediction, define representative training and evaluation data and how changing input patterns will be detected.
What to put in an AI development brief
A useful brief makes the intended outcome testable. House Hint is related evidence of AI-assisted matching in a property product; Flux Foundry shows LLM-assisted industrial data enrichment. These are different applications, so your evaluation should follow your own task.
- One user problem and an example of a successful result.
- Available data, its owner, and permission to process it.
- A simpler baseline for comparing quality and operating cost.
- Human review requirements and unacceptable failure cases.
- Acceptance criteria, recurring costs, and the person responsible after launch.