The 7 Fallacies in Gen AI

1. One-shot generation
This fallacy assumes that a single call to a generative AI model can produce complete, accurate, and usable output. It overlooks the importance of iterative refinement, feedback loops, or multi-turn reasoning to improve quality and correctness.
Example: Asking an LLM to generate a production-ready flow from just a high-level title, expecting no bugs or missing steps.
2. Speed over substance
Prioritizing fast response times can compromise depth, accuracy, or alignment with intent. Speed may impress, but without substance, the output often requires rework or manual correction.
Example: Generating a task automation flow in under 2 seconds that skips required validations or roles, leading to operational errors.
3. Zero preprocessing
This fallacy ignores the need for preparing structured context, summaries, or embeddings before using Gen AI. It assumes the model knows enough from raw or incomplete inputs, which often leads to shallow results.
Example: Running flow generation without first summarizing related subflows and actions available in the system.
4. Minimal input sufficiency
Believing that minimal prompts like function names or keywords are enough can lead to vague or wrong outputs. Gen AI benefits greatly from detailed semantics, relationships, and use case context.
Example: Expecting the phrase "process user request" to generate a complete flow without describing user types or request types.
5. No reasoning needed
Skipping intermediate reasoning steps assumes that the LLM can jump from problem to solution directly. In reality, breaking down tasks, planning, and chaining logic improves output quality.
Example: Asking a bot to recommend a workflow change without analyzing current inefficiencies or dependencies first.
6. Prompt has all the answers
Relying solely on user prompts assumes they contain all the required clarity and detail. This fallacy overlooks ambiguity, missing information, and the need to fetch or infer additional context.
Example: A vague prompt like “optimize employee onboarding” without knowing the current process steps or metrics.
7. Post-generation checks are optional
Assuming LLM outputs are inherently trustworthy leads to skipping validation, testing, or human-in-the-loop review. Guardrails and review mechanisms are critical to avoid silent failures.
Example: Deploying a Gen AI-generated flow to production without testing edge cases or verifying role access controls.