GEMINI AI — POTENTIAL ERRORS AND LIMITATIONS
1. Hallucination / generating false information
2. Giving incorrect answers with excessive confidence
3. Fabricating citations or non-existent sources
4. Misrepresenting the content of real sources
5. Using outdated information
6. Misunderstanding the context of a question
7. Forgetting or ignoring information from earlier in a long conversation
8. Making errors in complex multi-step reasoning
9. Failing to identify logical fallacies
10. Accepting a false premise and building an apparently valid conclusion upon it
11. Mathematical and calculation errors
12. Misinterpreting scientific information
13. Confusing correlation with causation
14. Incorrectly interpreting ambiguous questions
15. Partially or incorrectly following specific user instructions
16. Failing to follow the requested output format or constraints
17. Giving inconsistent answers to the same question at different times
18. Showing excessive confidence about its own capabilities
19. Failing to communicate uncertainty appropriately
20. Making assumptions when sufficient information is unavailable
21. Selecting weak or unreliable sources during research
22. Over-relying on secondary sources instead of primary sources
23. Omitting contradictory evidence
24. Potentially exhibiting confirmation-bias-like behavior
25. Potential framing bias in political topics
26. Potential framing bias in religious topics
27. Potential cultural or linguistic bias
28. Inconsistent safety or refusal behavior for similar requests
29. Unnecessary refusal of harmless questions
30. Providing insufficient caution when responding to sensitive topics
31. Misidentifying visual details during image analysis
32. OCR / text-recognition errors in images
33. Misinterpreting charts and graphs
34. Making assumptions beyond what is actually visible in an image
35. Transcription or interpretation errors in audio/video analysis
36. Overlooking important information in PDFs or documents
37. Syntax or runtime errors in generated code
38. Logically incorrect code
39. Using outdated libraries or APIs
40. Potentially generating code containing security vulnerabilities
41. Failing to account for edge cases
42. Claiming code is correct without actually verifying it
43. Changing meaning or context during translation
44. Grammar, idiom, or contextual errors in Bengali
45. Misunderstanding context-dependent meanings of words
46. Errors when retrieving information from long documents
47. Failing to properly reconcile conflicting information from multiple sources
48. Strong benchmark performance not necessarily translating into equally strong real-world performance
49. Capabilities and limitations changing across different model versions
50. Treating search results or retrieved information as factual without sufficient verification
51. Losing track of the user’s actual objective
52. Producing unnecessarily verbose answers
53. Omitting important information and producing incomplete answers
54. Self-contradiction within the same response
55. False precision — giving overly specific answers when the evidence is uncertain
56. Being misled by subtle prompt wording
57. Potentially producing incorrect conclusions under adversarial or misleading prompts
58. Errors concerning recent events due to knowledge limitations or insufficiently updated information
59. Presenting independently unverified information as factual
60. Like any AI system, it cannot guarantee 100% accuracy, neutrality, or logical correctness