Generated on 2025-12-10 23:10:14
✅ Ollama is running with ministral-3:8b
This demo compares all optimization modes across test fixtures.
Original: 44 tokens
I am working on a project that I need to complete for tomorrow. I need to
research the TAM of hot tubs. Organize by region in the United States. Include a
breakdown of top vendors in each region.
Tokens: 40 (-9.1%)
[GOAL]
I need to research the TAM of hot tubs.
[CONSTRAINTS]
- Organize by region in the United States.
- Include a breakdown of top vendors in each region
Tokens: 29 (-34.1%)
Research **Total Addressable Market (TAM) for hot tubs in the U.S.**, segmented
by region with top vendors in each.
Tokens: 24 (-45.5%)
Research U.S. hot tub **Total Addressable Market (TAM)** by region, including
top vendors per region.
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 44 | 0% | - |
| Rules-Only | 40 | 9.1% | Pattern-based |
| Standard | 29 | 34.1% | LLM on original |
| Combined | 24 | 45.5% | Rules → LLM |
Original: 155 tokens
Hi ChatGPT!
Sorry for the long prompt, but I need your help. I'm working on a project and
need to analyze customer feedback data.
The task is to categorize customer reviews into sentiment categories. It must be
accurate. You should not ignore edge cases. The output must be in JSON format
with the following structure:
{
"sentiment": "positive|negative|neutral",
"confidence": 0.0-1.0,
"category": "product|service|support"
}
For example: A review saying "Great product, fast shipping!" would be
categorized as positive sentiment with high confidence.
Do not include reviews that are spam or irrelevant. The analysis should focus on
genuine customer feedback.
Thanks so much for your help!
Tokens: 154 (-0.6%)
[GOAL]
I need your help. I'm working on a project and need to analyze customer feedback
data. The task is to categorize customer reviews into sentiment categories.
[CONSTRAINTS]
- It must be accurate.
- You should not ignore edge cases.
- The output must be in JSON format with the following structure: { "sentiment": "positive|negative|neutral", "confidence": 0.0-1.0, "category": "product|service|support"
- Do not include reviews that are spam or irrelevant.
- The analysis should focus on genuine customer feedback.
[OUTPUT FORMAT]
json
[EXAMPLES]
1. A review saying "Great product, fast shipping!" would be categorized as
positive sentiment with high confidence.
Tokens: 52 (-66.5%)
Analyze customer reviews for sentiment (positive/negative/neutral) with
confidence scores (0.0–1.0) and categorize by type (product/service/support) in
JSON format. Exclude spam/irrelevant entries. Ensure edge cases are handled.
Tokens: 87 (-43.9%)
Analyze customer reviews into sentiment categories (positive/negative/neutral)
with confidence scores (0.0–1.0) and category (product/service/support) in JSON
format: { "sentiment": "positive|negative|neutral", "confidence": float,
"category": "product|service|support" }. Exclude spam/irrelevant reviews;
prioritize genuine feedback. No output for invalid entries.
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 155 | 0% | - |
| Rules-Only | 154 | 0.6% | Pattern-based |
| Standard | 52 | 66.5% | LLM on original |
| Combined | 87 | 43.9% | Rules → LLM |
Original: 102 tokens
I’m trying to deploy my app on Kubernetes and I followed some tutorials but my
pods keep restarting and I don’t know what the logs mean. Also I think maybe my
YAML is wrong or I need more replicas? I don’t know. Should everything be in one
file or many? And do I need Helm for this or is that overkill? Also what’s the
deal with Ingress vs LoadBalancer? How do I make a simple deployment that “just
works”?
Tokens: 108 (--5.9%)
[GOAL]
I’m trying to deploy my app on Kubernetes and I followed some tutorials but my
pods keep restarting and I don’t know what the logs mean. Also my YAML is wrong
or I need more replicas? I don’t know. file or many? And do I need Helm for this
or is that overkill? Also what’s the deal with Ingress vs LoadBalancer? How do I
make a simple deployment that “just works”?
[CONSTRAINTS]
- Should everything be in one
[OUTPUT FORMAT]
yaml
Tokens: 50 (-51.0%)
"Debug Kubernetes pod restarts by interpreting logs and fixing YAML issues.
Clarify if a single-file or modular YAML structure is optimal. Determine if Helm
is necessary. Explain Ingress vs. LoadBalancer differences. Provide a minimal,
functional deployment setup."
Tokens: 80 (-21.6%)
Create a simple Kubernetes deployment with one pod (no replicas) using a Deployment YAML that:
1. Runs a basic container (e.g., `nginx`).
2. Logs errors clearly.
3. Uses `LoadBalancer` for external access (no Helm).
4. Minimal YAML for clarity.
Optimize for debugging: Add `restartPolicy: Always` and `livenessProbe` for troubleshooting.
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 102 | 0% | - |
| Rules-Only | 108 | -5.9% | Pattern-based |
| Standard | 50 | 51.0% | LLM on original |
| Combined | 80 | 21.6% | Rules → LLM |
Original: 70 tokens
I'm trying to make some FHIR resources but I don't know if I'm supposed to
make profiles or extensions or just put data in fields. For example I have
a medication order and some labs but I don't know where anything goes. Also
what's the best way to reference other resources? Is there a right folder
structure for IGs?
Tokens: 74 (--5.7%)
[GOAL]
I'm trying to make some FHIR resources but I don't know if I'm supposed to
make profiles or extensions or just put data in fields. For example I have
a medication order and some labs but I don't know where anything goes. Also
what's the best way to reference other resources? Is there a right folder
structure for IGs?
Tokens: 86 (--22.9%)
How to structure FHIR resources: medication orders, labs, and references—use
profiles for custom constraints, extensions for additional data, and standard
fields for core info. Reference other resources via `Reference` elements
(e.g., `patient`, `observation`). For Implementation Guides (IGs), organize
folders by resource type (e.g., `/resources/medication-request`,
`/resources/observation`) with profiles/extensions in subfolders.
Tokens: 26 (-62.9%)
How to structure FHIR resources: medication orders, labs, and references?
Optimize folder structure for Implementation Guides (IGs).
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 70 | 0% | - |
| Rules-Only | 74 | -5.7% | Pattern-based |
| Standard | 86 | -22.9% | LLM on original |
| Combined | 26 | 62.9% | Rules → LLM |
Original: 32 tokens
Good morning! Honestly, to be honest, I really think this is quite very
complicated. I basically need help understanding async functions in Python.
Thanks so much!
Tokens: 8 (-75.0%)
[GOAL]
async functions in Python
Tokens: 6 (-81.2%)
Understand Python async functions.
Tokens: 56 (--75.0%)
Write concise Python `async` function examples covering:
- Basic `async/await` syntax
- Running coroutines
- Error handling
- Concurrent execution with `asyncio.gather()`
- Timeouts with `asyncio.wait_for()`
- Key differences from synchronous code
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 32 | 0% | - |
| Rules-Only | 8 | 75.0% | Pattern-based |
| Standard | 6 | 81.2% | LLM on original |
| Combined | 56 | -75.0% | Rules → LLM |
Original: 120 tokens
Hi there! I'm working on a Python script and need some guidance. Sorry if this
is a bit messy. I want to create a function that processes customer data from a
CSV file. The function must handle missing values gracefully. It should not fail
if a column is missing. You need to return a pandas DataFrame. Do not use any
deprecated pandas methods. Make sure the code is well-documented with
docstrings. For example: if the 'email' column is missing, just skip it rather
than raising an error. The output should follow PEP 8 style guidelines. Thanks
so much!
Tokens: 116 (-3.3%)
[GOAL]
I want to create a function that processes customer data from a CSV file.
[CONSTRAINTS]
- The function must handle missing values gracefully.
- It should not fail if a column is missing.
- You need to return a pandas DataFrame.
- Do not use any deprecated pandas methods.
- Make sure the code is well-documented with docstrings.
- The output should follow PEP 8 style guidelines
[OUTPUT FORMAT]
csv
[EXAMPLES]
1. if the 'email' column is missing, just skip it than raising an error.
Tokens: 33 (-72.5%)
Create a Python function to process CSV customer data into a pandas DataFrame,
gracefully skipping missing columns/values without deprecated methods, with
docstrings and PEP 8 compliance.
Tokens: 36 (-70.0%)
Create a Python function processing CSV customer data into a pandas DataFrame,
handling missing columns/values gracefully, using only non-deprecated methods,
with PEP 8-compliant docstrings.
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 120 | 0% | - |
| Rules-Only | 116 | 3.3% | Pattern-based |
| Standard | 33 | 72.5% | LLM on original |
| Combined | 36 | 70.0% | Rules → LLM |
Original: 80 tokens
Hello! I really need your assistance with something. I'm trying to understand
quantum mechanics for a school project. Can you explain quantum entanglement in
simple terms? It would be great if you could avoid complex mathematics. I need
this to be accessible for someone without a physics background. Maybe include an
analogy or example? That would be super helpful. Thank you very much for your
time and help!
Tokens: 86 (--7.5%)
[GOAL]
I need your assistance with something. I'm trying to understand quantum
mechanics for a school project. Can you explain quantum entanglement in simple
terms? I need this to be accessible for someone without a physics background.
That would be super helpful. Thank you much for your time and help
[CONSTRAINTS]
- It would be great if you could avoid complex mathematics.
- include an analogy or example?
Tokens: 26 (-67.5%)
"Explain quantum entanglement in simple terms for a non-physics background,
using an analogy or example without complex math."
Tokens: 24 (-70.0%)
Explain quantum entanglement in simple terms for a non-physics background, using
an analogy or example without math.
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 80 | 0% | - |
| Rules-Only | 86 | -7.5% | Pattern-based |
| Standard | 26 | 67.5% | LLM on original |
| Combined | 24 | 70.0% | Rules → LLM |
Original: 72 tokens
“I want to add a new React feature: a reusable component that fetches data,
displays loading and error states, and exposes a render-prop or
children-as-function API so consumers can decide how to display the results. How
should I structure my components, hooks, and modules so the feature is testable,
composable, and avoids prop-drilling hell?”
Tokens: 81 (--12.5%)
[GOAL]
“I want to add a new React feature: a reusable component that fetches data,
displays loading and error states, and exposes a render-prop or
children-as-function API so consumers can decide how to display the results.
[CONSTRAINTS]
- How should I structure my components, hooks, and modules so the feature is testable, composable, and avoids prop-drilling hell?”
Tokens: 42 (-41.7%)
"Design a reusable React component for fetching data with loading/error states
using render-prop or children-as-function. Structure components, hooks, and
modules to ensure testability, composability, and avoid prop-drilling."
Tokens: 35 (-51.4%)
"Design a reusable React component for fetching data with loading/error states
using render-prop or children-as-function, structured for testability,
composability, and avoiding prop-drilling."
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 72 | 0% | - |
| Rules-Only | 81 | -12.5% | Pattern-based |
| Standard | 42 | 41.7% | LLM on original |
| Combined | 35 | 51.4% | Rules → LLM |
Original: 11 tokens
Hey GPT, can you help me? Thanks!
Tokens: 9 (-18.2%)
[GOAL]
can you help me?
Tokens: 3 (-72.7%)
Help me.
Tokens: 3 (-72.7%)
Help me.
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 11 | 0% | - |
| Rules-Only | 9 | 18.2% | Pattern-based |
| Standard | 3 | 72.7% | LLM on original |
| Combined | 3 | 72.7% | Rules → LLM |
Original: 25 tokens
Hey GPT, can you please help me write a summary? It needs to be short and informative. Thanks so much!
Tokens: 13 (-48.0%)
[GOAL]
can you please help me write a summary?
Tokens: 7 (-72.0%)
Write a concise, informative summary.
Tokens: 8 (-68.0%)
Summarize [context/key points].
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 25 | 0% | - |
| Rules-Only | 13 | 48.0% | Pattern-based |
| Standard | 7 | 72.0% | LLM on original |
| Combined | 8 | 68.0% | Rules → LLM |
Original: 71 tokens
I am working on a project and I need to add a new feature to my TypeScript
codebase: a set of utility functions for fetching, transforming, and caching
domain objects. How should I structure the folders, modules, and shared types so
the feature is cleanly isolated, easy to test, and doesn't introduce circular
dependencies? Thanks
Tokens: 75 (--5.6%)
[GOAL]
I am working on a project and I need to add a new feature to my TypeScript
codebase: a set of utility functions for fetching, transforming, and caching
domain objects. the feature is cleanly isolated, easy to test, and doesn't
introduce circular dependencies?
[CONSTRAINTS]
- How should I structure the folders, modules, and shared types so
Tokens: 33 (-53.5%)
"Suggest a clean TypeScript folder/module structure for utility functions
handling domain object fetching, transformation, and caching—ensuring isolation,
testability, and no circular dependencies."
Tokens: 37 (-47.9%)
"Suggest a clean folder/module structure and shared type design for TypeScript
utility functions handling domain object fetching, transformation, and
caching—ensuring isolation, testability, and no circular dependencies."
| Mode | Tokens | Reduction | Method |
|---|---|---|---|
| Original | 71 | 0% | - |
| Rules-Only | 75 | -5.6% | Pattern-based |
| Standard | 33 | 53.5% | LLM on original |
| Combined | 37 | 47.9% | Rules → LLM |
Mode Comparison:
- 🔧 Rules-Only: Fast, deterministic, pattern-based optimization
- 🤖 Standard: LLM processes original prompt directly
- 🔀 Combined: Rules first, then LLM (best of both worlds)
Usage:
# Rules-only (no LLM required)
bun run lessprompt --mode rules-only "Your prompt"
# Standard mode (LLM on original)
bun run lessprompt --mode standard "Your prompt"
# Combined mode (Rules → LLM)
bun run lessprompt --mode combined "Your prompt"