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 Using outdated libraries or APIs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