K-Dense Web is a powerful task execution engine, but like any tool, getting the best results depends on how you use it. The difference between a vague prompt and a well-structured one can mean the difference between "pretty good" results and exactly what you need.
This guide covers the six key elements of an effective K-Dense Web prompt. Master these, and you'll consistently get better, faster, more actionable results.
The Six Elements of an Effective Prompt
Every great K-Dense Web prompt addresses these six areas:
- Clear Objective - What do you want to achieve?
- Data Source - Where is your data coming from?
- Deliverables - What outputs do you need?
- Method Preferences - Any specific approaches or tools?
- Target Audience - Who will consume the results?
- Additional Context - What else might help?
Let's break down each one with examples.
1. Clear Objective
Why it matters: K-Dense Web breaks down your task into actionable steps. A vague objective leads to generic results. A specific objective leads to targeted, useful outputs.
❌ Vague Objective
Analyze my data and tell me what's interesting.
✅ Clear Objective
Identify the top 5 factors that predict customer churn
in our SaaS product, and quantify the impact of each factor
on 90-day retention rates.
Tips for Writing Clear Objectives
- Be specific about the outcome you need, not just the general topic
- Include success criteria when possible (e.g., "achieve at least 85% accuracy")
- State the business question you're trying to answer
- Mention constraints like timeline, budget, or regulatory requirements
More Examples
| Vague | Clear |
|---|---|
| "Analyze sales data" | "Identify seasonal patterns in Q1-Q4 sales and forecast Q1 2027 revenue with confidence intervals" |
| "Help with my research" | "Conduct a systematic literature review on CRISPR delivery mechanisms, focusing on papers from 2023-2026" |
| "Look at this dataset" | "Build a classification model to predict loan defaults using the attached credit data, optimizing for precision to minimize false positives" |
2. Clear Data Source
Why it matters: K-Dense Web can work with data you upload, publicly available datasets, or synthetic data it generates. Being explicit about your data source prevents confusion and ensures the analysis uses the right inputs.
Data Source Options
| Source Type | When to Use | How to Specify |
|---|---|---|
| Uploaded Data | You have proprietary or specific data | "Use the attached CSV file containing our customer transactions" |
| Public Data | Standard datasets or open sources | "Use the UCI Heart Disease dataset" or "Pull S&P 500 data from Yahoo Finance" |
| Synthetic Data | Prototyping, demos, or when real data isn't available | "Generate a synthetic dataset of 10,000 patient records with realistic distributions" |
| Web Sources | Current information needed | "Gather data from recent SEC filings for Fortune 500 tech companies" |
Any Format, Any Source
K-Dense Web can read and process any file format supported by open-source tools. This includes:
| Category | Supported Formats |
|---|---|
| Tabular Data | CSV, TSV, Excel (.xlsx, .xls), Parquet, Feather, HDF5, SQLite, JSON, XML |
| Documents | PDF, Word (.docx), PowerPoint (.pptx), Markdown, HTML, LaTeX, RTF |
| Scientific | MATLAB (.mat), SAS (.sas7bdat), Stata (.dta), SPSS (.sav), NetCDF, FITS |
| Geospatial | Shapefile, GeoJSON, KML, GeoTIFF, GPX |
| Images | PNG, JPEG, TIFF, SVG, DICOM (medical imaging) |
| Code & Config | Python (.py), R (.R), Jupyter (.ipynb), YAML, JSON, TOML, SQL |
| Compressed | ZIP, TAR, GZIP, 7z (automatically extracted) |
| Domain-Specific | FASTA/FASTQ (genomics), PDB (proteins), VCF (variants), and more |
If Python or R can read it, K-Dense Web can work with it. Don't hesitate to upload specialized file formats—just describe what the file contains in your prompt.
Example Prompts by Data Source
Uploaded Data:
Using the attached sales_data.xlsx file, analyze regional
performance trends and identify underperforming territories.
The file contains columns for date, region, product_category,
revenue, and units_sold.
Public Data:
Using the Kaggle Titanic dataset, build a survival prediction
model and explain which features are most important.
Synthetic Data:
Generate a realistic synthetic dataset of e-commerce transactions
(~50,000 rows) with customer demographics, purchase history, and
churn labels. Then build a churn prediction model using this data.
Combined Sources:
Combine our internal customer data (attached) with publicly
available census data for demographic enrichment, then segment
customers by predicted lifetime value.
Pro Tip: Describe Your Data
When uploading data, briefly describe what's in it:
The attached dataset (clinical_trial_results.csv) contains:
- 2,847 patient records from our Phase 2 trial
- Columns: patient_id, age, sex, treatment_arm, baseline_score,
week4_score, week12_score, adverse_events, dropout_flag
- Primary endpoint: change in score from baseline to week 12
This helps K-Dense Web understand your data faster and apply the right analysis methods.
3. Clear Deliverables
Why it matters: K-Dense Web can generate many types of outputs—reports, code, presentations, visualizations, papers, and more. Specifying exactly what you need ensures you get usable results without extra back-and-forth.
Any Output Format You Need
Just as K-Dense Web can read any file format, it can also generate outputs in any format producible by open-source tools:
| Output Type | Available Formats |
|---|---|
| Documents | PDF, Word (.docx), Markdown, HTML, LaTeX, RTF |
| Presentations | PowerPoint (.pptx), PDF slides, HTML slides (reveal.js) |
| Spreadsheets | Excel (.xlsx), CSV, Parquet, JSON |
| Visualizations | PNG, SVG, PDF (vector), interactive HTML (Plotly, Bokeh) |
| Code | Python scripts, Jupyter notebooks, R scripts, SQL queries |
| Data Exports | Any tabular format, serialized models (.pkl, .joblib), ONNX |
Need a specific format? Just ask. If there's an open-source library that can produce it, K-Dense Web can generate it.
Specify These Details
- Output type(s): Report, presentation, code, paper, figures, etc.
- Quantity: How many visualizations, slides, or pages?
- Format preferences: PDF, PowerPoint, Python notebook, Word doc?
- Level of detail: Executive summary vs. comprehensive technical report?
Example Deliverable Specifications
Minimal (okay):
Generate a report with visualizations.
Better:
Generate:
1. An executive summary (1 page) with key findings
2. A detailed technical report (5-10 pages) with methodology
3. 5-7 publication-quality figures
4. The Python code used for analysis (Jupyter notebook)
Best:
Deliverables needed:
1. Executive presentation (10-12 slides, PowerPoint format)
for board meeting - focus on business impact
2. Technical appendix (PDF) with full statistical methodology
and assumptions
3. Interactive dashboard mockup showing key metrics
4. Reproducible Python code (Jupyter notebook) with comments
5. One-page summary suitable for press release
Common Deliverable Types
| Type | Best For | Typical Specification |
|---|---|---|
| Report | Comprehensive analysis | "10-15 page report with executive summary" |
| Presentation | Stakeholder communication | "12-15 slides, suitable for non-technical audience" |
| Code | Reproducibility, deployment | "Jupyter notebook with documented functions" |
| Paper | Academic publication | "Formatted for Nature Methods, ~3000 words" |
| Figures | Publication, reports | "5-7 figures, 300 DPI, suitable for print" |
| Dashboard | Ongoing monitoring | "Interactive dashboard with key KPIs" |
4. Method Preferences
Why it matters: K-Dense Web has access to a wide range of analytical methods, Python packages, and data sources. If you have preferences—or requirements—stating them upfront ensures the analysis aligns with your needs.
When to Specify Methods
- Regulatory requirements: "Must use FDA-accepted statistical methods"
- Organizational standards: "We use scikit-learn for all ML models"
- Reproducibility: "Use only packages available in our production environment"
- Interpretability: "Prefer interpretable models (logistic regression, decision trees) over black-box approaches"
- Specific techniques: "Apply SHAP values for feature importance"
Example Method Specifications
Statistical Preferences:
Use parametric tests where assumptions are met; otherwise
fall back to non-parametric alternatives. Report effect sizes
and confidence intervals, not just p-values.
Package Preferences:
Use pandas and scikit-learn for data processing and modeling.
For visualization, use matplotlib and seaborn (not plotly).
For statistical tests, use scipy.stats.
Methodology Preferences:
For the survival analysis, use Cox proportional hazards models.
Check the proportional hazards assumption and use stratification
if violated. Report hazard ratios with 95% CIs.
Source Preferences:
For literature review, prioritize peer-reviewed sources from
PubMed and Google Scholar. Include preprints from bioRxiv only
if directly relevant. Exclude sources older than 2020.
If You Don't Have Preferences
That's fine too! K-Dense Web will select appropriate methods based on your data and objectives. You can simply say:
Use whatever methods are most appropriate for this analysis.
Explain your methodology choices in the report.
5. Target Audience
Why it matters: A report for executives looks very different from one for data scientists. Specifying your audience helps K-Dense Web calibrate the technical depth, language, and focus of the outputs.
Audience Dimensions to Consider
- Technical level: Expert, intermediate, non-technical
- Role: Executive, researcher, engineer, regulator, investor
- Domain familiarity: Industry expert vs. general business audience
- Decision context: What decision will this inform?
Example Audience Specifications
Executive Audience:
Target audience: C-suite executives with limited technical
background. Focus on business implications and ROI. Minimize
jargon. Lead with recommendations, then supporting evidence.
Technical Audience:
Target audience: Data science team for peer review. Include
full methodology, code, and statistical details. Assume
familiarity with ML concepts and Python.
Regulatory Audience:
Target audience: FDA reviewers for IND submission. Follow
ICH E9 guidelines for statistical reporting. Include all
required tables and figures per agency guidance.
Mixed Audience:
Two audiences: (1) Executive summary for leadership - focus
on strategic implications, (2) Technical appendix for
engineering team - include implementation details and code.
6. Additional Context
Why it matters: The more relevant context you provide, the better K-Dense Web can tailor its approach. This includes attachments, prior work, constraints, and success criteria.
Types of Additional Context
Prior Work
We previously analyzed this dataset in Q2 (see attached
Q2_analysis.pdf). Build on those findings—don't repeat
the exploratory analysis, focus on the predictive modeling.
Data Documentation
Attached: data_dictionary.xlsx explaining all column
definitions and valid values. Also attached:
study_protocol.pdf with the experimental design.
Code Files (Python, R, etc.)
Attached: preprocessing_pipeline.py - this is our current
data cleaning code. Please use the same transformations
for consistency. Also see feature_engineering.R for the
derived variables we've already validated.
Reference code attached:
- baseline_model.ipynb: Our current production model (beat this)
- utils.py: Helper functions for our data format
- config.yaml: Feature definitions and thresholds we use
Reference Documents (PDFs, Papers, Reports)
Key references attached:
- smith_et_al_2024.pdf: The methodology we want to replicate
- FDA_guidance_SAMD.pdf: Regulatory requirements to follow
- competitor_whitepaper.pdf: Benchmark we need to exceed
Please review the attached materials:
- literature_review.pdf: Summary of 50 relevant papers
- domain_expert_notes.pdf: SME feedback on initial analysis
- previous_submission_feedback.pdf: Reviewer comments to address
Presentations and Slide Decks
Attached: Q3_board_presentation.pptx - this is the format
and style leadership expects. Match this design language
for the new presentation.
Reference slides attached:
- investor_deck_template.pptx: Use this template
- competitor_pitch.pdf: What we're positioning against
- brand_guidelines.pdf: Color palette and fonts to use
Constraints and Requirements
Constraints:
- Analysis must be reproducible with Python 3.10+
- Cannot use cloud APIs (all processing must be local)
- Results needed by Friday for board presentation
- Budget for compute: keep under 100 GPU-hours
Optimization Criteria
Optimize for:
- Precision over recall (false positives are costly)
- Model interpretability (need to explain to regulators)
- Inference speed (model will run in production at 1000 QPS)
Domain-Specific Context
Context: This is for a medical device submission. All
statistical methods must align with FDA guidance for
AI/ML-based Software as a Medical Device (SaMD).
See attached FDA guidance document.
What Success Looks Like
Success criteria:
- Model AUC > 0.85 on held-out test set
- Identify at least 3 actionable feature engineering opportunities
- Generate investor-ready visualizations
- Complete analysis within 4 hours
Attachment Quick Reference
Remember: K-Dense Web can handle any file format readable by open-source tools. Here are common attachment types:
| Attachment Type | Examples | Why It Helps |
|---|---|---|
| Tabular data | .csv, .xlsx, .parquet, .json, .sas7bdat, .dta | The actual data to analyze |
| Code files | .py, .R, .ipynb, .sql, .m (MATLAB) | Existing pipelines to build on or replicate |
| Documentation | .pdf, .docx, .md, .html | Data dictionaries, protocols, requirements |
| Reference papers | .pdf, .html | Methodologies to follow or replicate |
| Presentations | .pptx, .pdf, .key | Style templates and prior work |
| Config files | .yaml, .json, .toml, .ini | Feature definitions, thresholds, parameters |
| Images/Figures | .png, .jpg, .svg, .tiff, .dicom | Examples of desired visualization style |
| Scientific data | .mat, .nc, .fits, .fasta, .vcf, .pdb | Domain-specific formats (genomics, astronomy, etc.) |
| Geospatial | .shp, .geojson, .kml, .gpx | Geographic and mapping data |
| Archives | .zip, .tar.gz, .7z | Compressed collections (auto-extracted) |
Don't see your format? Upload it anyway—if Python or R can read it, K-Dense Web can process it.
Putting It All Together
Here's an example of a well-structured prompt that incorporates all six elements:
OBJECTIVE:
Build a predictive model for hospital readmission within 30 days
of discharge. Identify the top risk factors and quantify their
impact on readmission probability.
DATA SOURCE:
Using the attached patient_data.csv file containing 50,000
discharge records from 2023-2025. Columns include demographics,
diagnosis codes, length of stay, prior admissions, and
readmission flag. See attached data_dictionary.xlsx for
column definitions.
DELIVERABLES:
1. Executive summary (2 pages) for hospital leadership
2. Technical report (10-15 pages) with full methodology
3. 6-8 publication-quality figures
4. Python code (Jupyter notebook) for reproducibility
5. One-page clinical decision support guide for care managers
METHOD PREFERENCES:
- Use XGBoost or LightGBM for the primary model
- Apply SHAP values for interpretability
- Use scikit-learn for preprocessing
- Report AUC, sensitivity, specificity, and calibration metrics
TARGET AUDIENCE:
Primary: Hospital quality improvement committee (clinical
background, limited ML expertise)
Secondary: Data science team (for technical validation)
ADDITIONAL CONTEXT:
Attachments included:
- patient_data.csv: Main dataset (50,000 records)
- data_dictionary.xlsx: Column definitions and valid values
- Q3_readmission_pilot.pdf: Prior analysis showing promising
results with length of stay and comorbidity count
- current_preprocessing.py: Our existing data cleaning pipeline
- cms_readmission_definitions.pdf: Official CMS methodology
- board_template.pptx: Slide format leadership expects
Additional notes:
- Optimize for sensitivity (catching high-risk patients is more
important than minimizing false positives)
- Must align with CMS Hospital Readmissions Reduction Program
definitions
- Results will inform a care management pilot program
Quick Reference Checklist
Before submitting your prompt, check that you've addressed:
| Element | Question to Ask | Included? |
|---|---|---|
| Objective | What specific outcome do I need? | ☐ |
| Data Source | Where is the data coming from? | ☐ |
| Deliverables | What outputs do I need, in what format? | ☐ |
| Methods | Any required or preferred approaches? | ☐ |
| Audience | Who will use these results? | ☐ |
| Context | What else would help? Attachments? Constraints? | ☐ |
The Bottom Line
K-Dense Web can execute remarkably complex tasks, but it works best when you're clear about what you need. Taking five minutes to structure your prompt with these six elements will save hours of iteration and deliver results that are immediately actionable.
Remember: You don't need to include every element for every task. Simple analyses might only need an objective and data source. Complex projects benefit from the full specification. Use your judgment.
The more context you provide, the better K-Dense Web can serve you. When in doubt, include it.
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Questions? Join our Slack community or reach out at contact@k-dense.ai.