Capabilities and Limitations of LLMs
Explore what LLMs genuinely excel at and where they fail, including hallucinations, knowledge cutoffs, and reasoning limits, to set realistic project expectations.
Capabilities and Limitations of LLMs is a free AI Engineering Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Engineering Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
What LLMs Genuinely Excel At
LLMs shine at language work: summarizing, translating, writing code, and reshaping text into formats like JSON. Give a couple examples and they catch the pattern fast.
Hallucination: The Fundamental Failure Mode
Hallucination is when a model confidently states something false. It predicts what sounds likely, not what's true — so never trust it as your only source of facts.
Knowledge Cutoffs and Outdated Information
Every model has a knowledge cutoff — it knows nothing after that date. For recent info, you feed it fresh documents at query time with RAG.
Reasoning Limits: Not a Logic Engine
LLMs mimic reasoning but aren't a true logic engine, so they slip on exact math and multi-step logic. For precise work, hand it to a real tool — see the code.
# Illustrating why you should use tools for computation
from openai import OpenAI
client = OpenAI()
# BAD: asking LLM to compute this directly
prompt_bad = 'What is 7.3% of 48,291.67?'
# GOOD: let Python compute, LLM just formats the answer
def calculate_percentage(value, pct):
return round(value * pct / 100, 2)
result = calculate_percentage(48291.67, 7.3)
print(f'7.3% of 48,291.67 is {result}') # 3525.29 - always correctContext Window as a Hard Constraint
The context window is the max tokens a model can handle at once — prompt, history, and output combined. Go over it and the request just fails.
Sensitivity to Prompt Wording
LLMs are sensitive to prompt wording. A tiny rephrase, or adding "think step by step," can change the answer a lot — powerful, but worth testing carefully.
Inconsistency and Non-Determinism
LLMs are non-deterministic: the same prompt can give different answers. A 95%-right model is still wrong 1 in 20 times, so test across many examples, not a few.
import openai
client = openai.OpenAI()
def sample_with_majority_vote(prompt, n=5):
responses = []
for _ in range(n):
r = client.chat.completions.create(
model='gpt-4o-mini',
messages=[{'role': 'user', 'content': prompt}],
temperature=0.3,
max_tokens=10
)
responses.append(r.choices[0].message.content.strip())
# Return most common answer
return max(set(responses), key=responses.count)Sycophancy: Agreement Bias
Sycophancy is when a model agrees with you even when you're wrong, because raters liked agreeable answers. For real critique, ask it to argue the other side.
What LLMs Cannot Do
Some limits are built in: an LLM can't browse, run code, recall past chats, or do exact math on its own. Pair it with tools for those jobs.
Bias and Representation Issues
Trained on internet text, LLMs pick up its biases — stereotypes, uneven language coverage, skewed views. Test across groups and document the limits for your users.
Setting Realistic Expectations for Projects
A slick demo can still fail on real inputs. That's not a reason to skip LLMs — it's why you build evaluation in from day one, measuring failures as you go.
Quick Check
Test your understanding of AI Engineering concepts from this lesson.
Lesson Recap
Recap: hallucination needs RAG or verification, knowledge cutoffs need retrieval, and non-determinism needs real evaluation. Next: your first OpenAI API call. 🎉
Frequently asked questions
Is the “Capabilities and Limitations of LLMs” lesson free?
Yes — the full text of “Capabilities and Limitations of LLMs” is free to read here on the web, and the AI Engineering Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Engineering Academy course, upgrade to CoddyKit PRO.
What will I learn in “Capabilities and Limitations of LLMs”?
Explore what LLMs genuinely excel at and where they fail, including hallucinations, knowledge cutoffs, and reasoning limits, to set realistic project expectations. You practise AI Engineering Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AI Engineering Academy?
No prior experience is required. AI Engineering Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Capabilities and Limitations of LLMs” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AI Engineering Academy lesson?
Yes. Every AI Engineering Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- From Autocomplete to ChatGPT
- Transformers and Attention in Plain English
- How LLMs Are Trained
- Capabilities and Limitations of LLMs