Writing complex prompts and constructing custom agents can feel like learning a new language. Conversational prompting removes that barrier: instead of wrestling with syntax, you simply describe what you need in plain English, and the system turns your instructions into structured prompt logic. The result is faster agent creation, fewer mistakes, and a clear path from idea to extraction-ready output.
What is conversational prompting?
Conversational prompting is an approach that converts natural language chat into the prompt logic an agent uses to extract or transform data. Rather than hand-coding dozens of prompt rules, you explain the goal, and the agent proposes a set of prompts and extraction steps you can review, edit, preview, and save.
Key benefits:
Accessibility: Anyone who can describe the task can build an agent, no prompt engineering expertise required.
Speed: Go from idea to working prompt logic in minutes.
Iterative control: Review and fine-tune generated prompts before committing them.
Template-driven: Upload output documents and let the agent generate the exact logic needed to fill them.
No need to worry about the syntax. You just chat with Kolena to generate the right prompt logic that you need."
Real-world example: Building a lease abstract from rent rolls
Here’s a practical workflow for turning a stack of rent roll documents into a structured lease abstract using conversational prompting.
Step 1: Describe the task in plain English
Start by telling the agent what you want. For example: “Can you help me generate a lease abstract from the uploaded rent roll documents?” No special formatting or complex instructions are required. The agent will think through the steps and propose a series of prompts that map to the required extraction logic.
Step 2: Review and edit the generated prompts
The agent presents the prompts as editable rows. This is where human judgment matters. Scan the proposed prompts and remove anything you don’t need (for example, monthly concessions), or add a new row for additional fields. Editing is immediate and intuitive.
Step 3: Preview the extracted output
Before you save, use the preview feature to see what the extracted output will look like. The preview shows exactly what data would be pulled from the source documents when the prompt logic runs. This catches mismatches early and reduces rework.
Step 4: Save and create agents
Once the prompts look right, save them and create the agent. You can create multiple prompts at once and build a structured agent ready to process incoming documents.
Try Our AI-Powered Lease Abstraction Tool
Shortcut: Upload an output template and auto-generate prompts
If you already have the final output format—a spreadsheet, a PDF template, or a report—you can upload that file directly. The agent will digest the document, identify each section, and generate corresponding prompts to populate the fields. It also detects redundancies by comparing with previously created prompts and skips duplicates. In short: one uploaded template + one chat = a fully structured agent that fills your document.
Why conversational prompting matters for lease abstraction
Faster turnaround: Lease abstraction often involves repeated manual extraction. Conversational prompting turns those repetitive tasks into reusable agents.
Lower friction: Non-technical staff can set up agents without a developer, freeing up technical resources for higher-value work.
Consistency: Using templates and previewed prompts ensures consistent outputs across documents and users.
Reduced error: Previewing and editing prompts before deployment minimizes extraction mistakes.
Practical tips for best results
Be specific about the fields you need. Clear instructions lead to clearer prompts.
Provide examples or a sample output when possible. The system can use the format to generate more accurate logic.
Use the preview to validate outputs before you commit to an agent.
Iterate quickly: If a prompt misses something, edit the row and preview again—small adjustments go a long way.
Upload templates: Let the agent digest your output document to auto-generate comprehensive prompt logic.
Quick checklist: From idea to agent
Describe your extraction task in plain English.
Review the generated prompt rows and remove or add fields as needed.
Use preview to validate sample extracted data.
Save and create the agent when satisfied.
Optionally upload an output template to auto-build prompts for every section.
Final thought
Conversational prompting transforms agent creation from a technical bottleneck into a conversational, iterative process. For tasks like lease abstraction where accuracy and speed matter, this approach makes it easy to go from plain English requirements to a fully functional agent that extracts the right data and fills the output you need.
