Start with the simple idea
Many GenAI failures come from repeated mistakes: trusting fluent output, skipping tests, using the wrong tool, or ignoring cost and safety.
Simple learning path: problem → intuition → mechanism → example → limits
What you will learn
- Explain Common GenAI Mistakes in plain language.
- Follow its mechanism step by step.
- Connect a small example to a real AI system.
- Recognize its strengths, limits, and common mistakes.
How this appears in current AI systems
Teams deploying GPT, Gemini, Claude, image generators, or open models evaluate the complete application, not only the base model, and add monitoring, guardrails, fallbacks, and human review according to risk.
Official grounding: OpenAI provides an evaluation guide, while Google documents Gemini safety settings. These sources support the evaluation and safety practices here; neither makes an AI application automatically correct or safe.
When this knowledge helps
Use Common GenAI Mistakes when it matches the problem described below. Before choosing it, check the task, available data, quality target, cost, response time, privacy, and safety needs; popularity alone is not a reason to use it.
1. The question this module answers
Every module in this course has flagged specific common mistakes in its own “Common Mistakes” section. This module gathers the really most important ones into one consolidated, practical reference — organized by theme, for quick review before building or shipping a real GenAI application.
2. Conceptual Mistakes
1. Conflating LLMs with Generative AI (Module 3): LLMs are ONE
class of
generative
model, focused
on language --
Generative AI
spans far more
2. Assuming generative models "just remix" existing
content (Module 2): they learn
statistical
patterns and
GENERALIZE,
really
different
from copying
fragments
3. Believing the oversimplified "AI contains ML contains
DL contains GenAI" nested circles (Module 3): these
are
really
different
KINDS of
categories,
not
cleanly
nested3. Generation and Sampling Mistakes
4. Using ONE fixed temperature/sampling setting for
EVERY task (Module 10, 14): match
sampling
strategy
to
whether
the
task
needs
CONSISTENCY
or
VARIETY
5. Treating image generation as a SINGLE-STEP, fast
instantaneous process (Module 13, 17): it
really
requires
MULTIPLE
sequential
denoising
steps
6. Writing VAGUE image-generation prompts and expecting
CONSISTENT results (Module 13): specificity
directly
shapes
what
conditioning
can
guide
generation
toward4. Code Generation Mistakes
7. Trusting generated code because it "looks right" or
compiles (Module 18): syntactic
validity
is NOT
the
same as
logical
correctness,
security,
or
full
test
coverage
8. Running freshly generated code directly against production
systems without sandboxing (Module 18, 29): a genuine,
real
safety
risk5. Cost and Infrastructure Mistakes
9. Assuming streaming reduces cost (Module 27): it ONLY changes
delivery timing,
not total tokens
billed
10. Not accounting for conversation history's COMPOUNDING
cost across turns (Module 27): input cost
grows with
EVERY turn
when
history is
resent in
full
11. Stuffing entire documents into every prompt
instead of using RAG (Module 27, 28): creates
really
avoidable
input
token
cost
12. Adopting heavyweight orchestration frameworks
for really simple applications (Module
(Module 24): 24):
simple,
direct
code is
often
perfectly
sufficient
13. Assuming self-hosting is automatically cheaper
(Module 26): real
infrastructure
costs
must
be
really
compared
against
actual
API
costs
at
your
specific
scaleAnalogy: The Novice DIY Builder Assembling Flat-Pack Furniture Think of committing common GenAI architectural anti-patterns like trying to assemble a complex Swedish wardrobe:
- Mistake 1: Using the wrong fasteners (Regex Parsing vs. Schema Constraints): Instead of using the designated locking pins and screws to connect structural boards (using strict JSON schema models), the novice tries to glue the joints together with duct tape (using complex custom regex post-processors to clean up model output). Eventually, the wardrobe falls apart under load.
- Mistake 2: The Sledgehammer for small screws (Overkill Model Selection): Using a heavy gasoline-powered jackhammer to screw in a 1-inch wood screw (dispatching a massive, multi-billion parameter proprietary model at $0.01/token for a simple binary classification task that a small, local open-source model could run for free in 50 milliseconds).
- Mistake 3: Forgetting to check if the floor is level (Ignoring Fallback Routing): Assembling the entire wardrobe on a slope without leveling legs (not setting API request timeout limits or alternative model fallback routing). The wardrobe leans and collapses.
📊 Visual Chart: Architectural Anti-Patterns vs. Best Practices
Here is the mapping from common implementation mistakes to their recommended production best practices:
graph TD
classDef bad fill:#e74c3c,stroke:#333,stroke-width:1px,color:#fff;
classDef good fill:#2ecc71,stroke:#333,stroke-width:1px,color:#fff;
subgraph AntiPatterns ["Common Anti-Patterns (Avoid)"]
RegexParse["1. Parse JSON with Regex post-processing"]:::bad
NoTimeout["2. Call LLM API directly without timeout limits"]:::bad
LargeModel["3. Use massive frontier models for simple classification"]:::bad
FullDoc["4. Stuff entire 100-page PDF into prompt context"]:::bad
end
subgraph BestPractices ["Production Best Practices (Adopt)"]
StructuredOut["1. Use Structured Outputs / JSON Schema constraints"]:::good
Fallback["2. Implement Circuit Breaker & Fallback Models"]:::good
ScaleDown["3. Deploy small, task-tuned local models (LoRA)"]:::good
RAGChunk["4. Implement semantic chunking & vector search (RAG)"]:::good
end
RegexParse -.->|Replace with| StructuredOut
NoTimeout -.->|Replace with| Fallback
LargeModel -.->|Replace with| ScaleDown
FullDoc -.->|Replace with| RAGChunk6. Hallucination and Reliability Mistakes
14. Assuming hallucination can be COMPLETELY eliminated
through better prompting alone (Module 32): it's a
structural
consequence
of how
generative
models
work --
mitigation,
not
elimination,
is
realistic
15. Using output FLUENCY/confidence as a signal
of correctness (Module 32): fluency
and
correctness
are
really
different
properties
16. Assuming RAG completely eliminates hallucination
(Module 28, 32): it
really
REDUCES
but does
not
eliminate
this risk7. Safety and Deployment Mistakes
17. Assuming alignment ALONE is sufficient for
a really high-stakes application (Module
(Module 33): 33):
necessary
but NOT
sufficient
alone
18. Applying uniform guardrails regardless of
actual stakes (Module 33): guardrail
intensity
should
REALLY
match
risk
19. Granting an agent broad autonomous action
without genuine safeguards (Module 29): sandboxing,
review,
and
explicit
boundaries
are
really
necessary8. A Real Developer Example — Applying This Catalog
A team is about to launch a document Q&A assistant. Walking through
this catalog as a PRE-LAUNCH CHECKLIST:
- Mistake #4 check: are sampling settings matched to the task
(factual Q&A -> low temperature)? YES, confirmed
- Mistake #11 check: is RAG used instead of stuffing entire
documents? YES, confirmed
- Mistake #14/15 check: does the system acknowledge uncertainty
rather than always confidently answering? NEEDS REVIEW -- add
explicit "if the context doesn't answer this, say so" instruction
- Mistake #17/18 check: is there a genuine guardrail beyond baseline
alignment for this specific application's stakes? NEEDS REVIEW --
this handles potentially sensitive internal documents, consider
ADDITIONAL access controls
This is EXACTLY how this consolidated catalog earns its practical
value -- not as an abstract list, but as a GENUINE, actionable
pre-launch review tool.9. A Simple Agentic AI Connection
Agent-specific mistakes from this catalog (items 8, 19) deserve particular attention precisely because agent systems combine MULTIPLE risk factors simultaneously — code generation risk (Module 18), autonomous action risk (Module 29), and compounding cost/latency (Modules 25, 27) all apply at once to a single agent workflow, making careful review against this catalog especially valuable before deploying any agent with real tool access.
10. How Is This Used in AI?
🤖 How Is This Used in AI?
A consolidated mistake catalog like this really functions as a practical pre-launch checklist for real GenAI teams — reviewing a new feature or application against a comprehensive, organized list of known failure patterns catches genuine issues before they reach real users, rather than discovering them reactively after deployment.
11. Common Mistakes (About This Catalog Itself)
Incorrect idea
Treating this catalog as exhaustive.
Why it is incorrect
New failure patterns really continue to emerge as the field evolves — this catalog reflects what’s been covered in this course, not every possible mistake.
Incorrect idea
Reviewing this catalog once and never revisiting it.
Why it is incorrect
As Module 4’s historical trajectory showed, the field continues to evolve — periodically revisiting this kind of checklist against a really evolving application and evolving best practices remains valuable.
12. Limitations
- This catalog reflects the mistakes covered across THIS specific course — it’s really comprehensive relative to this course’s scope, but not an absolute, universal list of every possible GenAI mistake
- Avoiding every item in this catalog doesn’t guarantee a really successful application — these are common FAILURE modes to avoid, not a complete recipe for success
13. Quick Reference — The Whole Idea in One Diagram
CONCEPTUAL: conflating LLM/GenAI, misunderstanding
generalization, oversimplified hierarchies
GENERATION: wrong sampling strategy, misjudging image-gen
speed, vague prompts
CODE: trusting generated code blindly, no
sandboxing
COST/INFRA: streaming=/=cheaper cost, compounding
history cost, no RAG, wrong tooling scale
RELIABILITY: hallucination "solved," fluency=/=
correctness, RAG=/=complete elimination
SAFETY: alignment alone insufficient,
uniform guardrails, unconstrained
agent autonomy14. Code — Building an Automated Pre-Launch Checklist
🎯 Target of this example: implement Section 8’s pre-launch checklist idea directly and observably — an automated review function that checks a GenAI application’s configuration against several mistakes from this catalog, flagging genuine issues before deployment.
Example 1 — Simple
def check_sampling_strategy(task_type: str, temperature: float) -> dict:
"""Checks against Mistake #4: wrong sampling strategy for task type."""
factual_tasks = {"data_extraction", "factual_qa", "classification"}
creative_tasks = {"creative_writing", "brainstorming"}
if task_type in factual_tasks and temperature > 0.3:
return {"passed": False, "issue": f"Task '{task_type}' needs LOW temperature "
f"for consistency, but temperature={temperature}"}
if task_type in creative_tasks and temperature < 0.5:
return {"passed": False, "issue": f"Task '{task_type}' would benefit from HIGHER "
f"temperature for variety, but temperature={temperature}"}
return {"passed": True, "issue": None}
checks = [
("data_extraction", 0.9), # WRONG -- factual task, high temp
("creative_writing", 0.8), # correct
]
for task_type, temp in checks:
result = check_sampling_strategy(task_type, temp)
status = "PASS" if result["passed"] else "FAIL"
print(f"[{status}] {task_type} (temp={temp}): {result['issue'] or 'OK'}")Expected Output:
[FAIL] data_extraction (temp=0.9): Task 'data_extraction' needs LOW
temperature for consistency, but temperature=0.9
[PASS] creative_writing (temp=0.8): OKWhat we conclude from this example: this simple check correctly catches Mistake #4 (wrong sampling strategy for the task) automatically — exactly the kind of genuine, automated pre-launch check a real team would want, rather than relying purely on manual review to catch this class of issue.
Example 2 — Intermediate
def check_rag_usage(has_large_documents: bool, uses_rag: bool) -> dict:
"""Checks against Mistake #11: stuffing entire documents instead of RAG."""
if has_large_documents and not uses_rag:
return {"passed": False, "issue": "Large documents detected without RAG -- "
"consider retrieval instead of full document stuffing."}
return {"passed": True, "issue": None}
def check_hallucination_mitigation(is_high_stakes: bool, has_uncertainty_instruction: bool) -> dict:
"""Checks against Mistake #14/15: no uncertainty acknowledgment for high-stakes tasks."""
if is_high_stakes and not has_uncertainty_instruction:
return {"passed": False, "issue": "High-stakes task lacks explicit "
"uncertainty-acknowledgment instructions."}
return {"passed": True, "issue": None}
def check_agent_guardrails(is_agent: bool, has_action_limits: bool) -> dict:
"""Checks against Mistake #19: unconstrained agent autonomy."""
if is_agent and not has_action_limits:
return {"passed": False, "issue": "Agent system lacks explicit action "
"authorization boundaries."}
return {"passed": True, "issue": None}
# Simulate a real application's configuration
app_config = {
"has_large_documents": True, "uses_rag": True,
"is_high_stakes": True, "has_uncertainty_instruction": False,
"is_agent": True, "has_action_limits": True,
}
checks = [
check_rag_usage(app_config["has_large_documents"], app_config["uses_rag"]),
check_hallucination_mitigation(app_config["is_high_stakes"], app_config["has_uncertainty_instruction"]),
check_agent_guardrails(app_config["is_agent"], app_config["has_action_limits"]),
]
for i, check in enumerate(checks, 1):
status = "PASS" if check["passed"] else "FAIL"
print(f"[{status}] Check {i}: {check['issue'] or 'OK'}")Expected Output:
[PASS] Check 1: OK
[FAIL] Check 2: High-stakes task lacks explicit
uncertainty-acknowledgment instructions.
[PASS] Check 3: OKWhat we conclude from this example: running multiple checks against a real application’s configuration correctly identifies ONE genuine issue (missing uncertainty instructions for a high-stakes task) while confirming two other areas are properly configured — exactly Section 8’s pre-launch review process, now automated and systematic rather than relying on someone remembering to manually check each item.
Example 3 — Production Grade
from dataclasses import dataclass, field
from enum import Enum
class Severity(Enum):
CRITICAL = "CRITICAL"
WARNING = "WARNING"
INFO = "INFO"
@dataclass
class ChecklistIssue:
mistake_reference: str
severity: Severity
description: str
@dataclass
class PreLaunchReport:
total_checks: int
issues: list = field(default_factory=list)
ready_to_launch: bool = True
class GenAIPreLaunchChecker:
"""A production-style, comprehensive checker running MULTIPLE
checks from this module's catalog, producing a structured report
with SEVERITY levels -- directly useful as real launch-gating
infrastructure for a GenAI team."""
def run_all_checks(self, config: dict) -> PreLaunchReport:
report = PreLaunchReport(total_checks=0)
# Check 1: sampling strategy (Mistake #4)
report.total_checks += 1
if config.get("task_type") in {"data_extraction", "factual_qa"} and config.get("temperature", 0) > 0.3:
report.issues.append(ChecklistIssue(
"Mistake #4", Severity.WARNING, "Factual task using high temperature."))
# Check 2: RAG usage (Mistake #11)
report.total_checks += 1
if config.get("has_large_documents") and not config.get("uses_rag"):
report.issues.append(ChecklistIssue(
"Mistake #11", Severity.WARNING, "Large documents without RAG -- avoidable cost."))
# Check 3: hallucination mitigation (Mistake #14/15) -- CRITICAL for high-stakes
report.total_checks += 1
if config.get("is_high_stakes") and not config.get("has_uncertainty_instruction"):
report.issues.append(ChecklistIssue(
"Mistake #14/15", Severity.CRITICAL, "High-stakes task without uncertainty handling."))
# Check 4: agent guardrails (Mistake #19) -- CRITICAL if agent has real actions
report.total_checks += 1
if config.get("is_agent") and config.get("has_real_world_actions") and not config.get("has_action_limits"):
report.issues.append(ChecklistIssue(
"Mistake #19", Severity.CRITICAL, "Agent with real-world actions lacks authorization boundaries."))
report.ready_to_launch = not any(issue.severity == Severity.CRITICAL for issue in report.issues)
return report
checker = GenAIPreLaunchChecker()
config = {
"task_type": "factual_qa", "temperature": 0.2, "has_large_documents": True,
"uses_rag": True, "is_high_stakes": True, "has_uncertainty_instruction": False,
"is_agent": True, "has_real_world_actions": True, "has_action_limits": False,
}
report = checker.run_all_checks(config)
print(f"Ready to launch: {report.ready_to_launch}")
print(f"Total checks run: {report.total_checks}, Issues found: {len(report.issues)}\\n")
for issue in report.issues:
print(f"[{issue.severity.value}] {issue.mistake_reference}: {issue.description}")Expected Output:
Ready to launch: False
Total checks run: 4, Issues found: 2
[CRITICAL] Mistake #14/15: High-stakes task without uncertainty
handling.
[CRITICAL] Mistake #19: Agent with real-world actions lacks
authorization boundaries.What we conclude from this example: the ready_to_launch flag
being automatically set to False because of two CRITICAL issues —
directly connecting to real-world action risk (Module 29, 33) and
high-stakes hallucination risk (Module 32, 33) — demonstrates exactly
how a real team could use this catalog as genuine, structural
launch-gating infrastructure, not just an informal reading list.
15. Interview Questions
Q: Why is it valuable to maintain a consolidated catalog of common GenAI mistakes, rather than relying on remembering individual lessons from throughout a course or career?
Ans: A consolidated catalog provides a genuine, systematic reference that can be reviewed methodically before launching or shipping a new feature, rather than relying purely on individual memory to catch each relevant issue. It also enables automation — checks against specific, named mistakes can be built into real pre-launch tooling, turning scattered lessons into structural, enforced safeguards rather than informal knowledge that might be inconsistently applied.
Q: Explain why “streaming reduces cost” and “RAG completely eliminates hallucination” are both really common misconceptions, and what the actual truth is in each case.
Ans: Streaming only changes when generated tokens are delivered to the user (progressively vs. all at once) — it doesn’t change the total number of tokens generated or billed, so it doesn’t reduce cost. Similarly, RAG really reduces hallucination risk by grounding generation in retrieved, verifiable context, but doesn’t eliminate it entirely — a model can still misread or inappropriately extrapolate beyond even the context it’s given. Both misconceptions share a pattern: mistaking a genuine, real improvement for a complete, absolute solution.
Q: Why might mistakes related to agents (like ungrounded autonomy or untested generated code) deserve particular attention in a pre-launch review?
Ans: Agent systems often combine multiple risk factors simultaneously — code generation risk, autonomous action risk, and compounding cost and latency across multiple sequential steps all apply at once within a single agent workflow. This makes agent-specific mistakes really higher-stakes to review carefully, since a single oversight (like missing action authorization boundaries) could compound across an agent’s entire multi-step task rather than affecting just one isolated generation.
Q: How would you design an automated pre-launch check for a specific mistake from this catalog, such as checking that sampling strategy matches the task type?
Ans: I’d define categories of task types with known appropriate sampling characteristics (factual/extraction tasks needing low temperature, creative tasks benefiting from higher temperature), then write a function that checks a given application’s configured temperature setting against its declared task type, flagging a mismatch as an issue. This turns an informal review guideline into a concrete, automatable check that could run as part of a genuine deployment pipeline, catching this specific class of mistake systematically rather than relying on manual review alone.
16. What You Should Remember
- This module consolidates 19 genuine mistakes flagged individually throughout the entire course, organized into six themes: conceptual, generation/sampling, code, cost/infrastructure, reliability, and safety.
- This catalog functions as a really practical pre-launch checklist — verified directly by building an automated checker that catches real configuration issues before deployment.
- Automating these checks where possible (as demonstrated with severity-tagged, structural launch-gating logic) turns scattered lessons into genuine, enforced safeguards, not just informal knowledge.
17. Quick Practice
Pick any three mistakes from this catalog that feel most relevant to a GenAI application you might build, and write out — in your own words — a specific, concrete check or safeguard you’d implement to catch each one before launch.
18. Next Step
Next: Module 35 — When to Use GenAI — a really honest look at when Generative AI is NOT the right tool, closing out this practical, production-focused stretch of the course.
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