Prompt AI
Precision Prompts, Predictable Outputs
Our Prompt AI service transforms text prompts into sophisticated, production-grade interfaces for large language models. We engineer structured prompt systems that deliver consistent, accurate, and auditable AI responses for enterprise applications. Unlike simple text inputs, our solutions implement rigorous prompting techniques. We develop dynamic prompt chaining systems that break complex queries into logical sequences. Our context-aware prompts automatically adapt based on user roles, data sensitivity, and output requirements. We have full version control and maintain audit trails of all prompt iterations and outputs.
Prompt Engineering for Enterprises
We build structured, modular prompt systems designed for consistent and reliable performance across enterprise use cases. By leveraging dynamic prompt chaining, we break down complex queries into logical, reusable components—ensuring clarity, adaptability, and traceability at every step.
Context-Aware Prompting
Our prompts adapt intelligently based on user roles, data sensitivity, and compliance requirements. Each prompt is designed with built-in safeguards and governance, including version control and audit trails, to maintain transparency, security, and accuracy across all interactions.
Built for Enterprise-Grade Use
Our enterprise-grade approach goes beyond prompt engineering. We embed governance, compliance, and operational safeguards into every layer of the system. From access controls and prompt security policies to validation checks and integration with existing workflows, our platform ensures Prompt AI operates safely at scale. Whether you're deploying across teams or embedding into high-stakes applications, we help you maintain full oversight and alignment with enterprise standards.
Chain-of-Thought Prompting
| CASES | INEFFICIENT PROMPT | IMPROVED PROMPT |
|---|---|---|
| Case I : Specific instructions in prompt. | “Extract all kinds of date ranges from resume.” | Identify Date Ranges:- Extract all date ranges (start date and end date) in the resume text. Date ranges can be in any format: • Month Year – Month Year (e.g., Jan 2020 – Dec 2021) • Year – Year (e.g., 2019 – 2020) • Month Year – Present (e.g., Mar 2022 – Present) • Month/Year or Numeric Formats (e.g., 01/20 – 12/21) |
| MODEL OUTPUT | Returned: - Jan 2020 - 2018 (single year from an award) - “Early 2022” (hallucinated) - Mar 2022 – Present | Returned: - Jan 2020 – Dec 2021 - Mar 2022 – Present |
| Issue: - Ambiguous: What kind of dates? - No format instructions - Didn’t mention handling “Present” or malformed ranges - Model returned single years or hallucinated date mentions | Improved: - Enforced format - Avoided hallucinations - Reduced noise in output | |
| Case II : Chain-of-thought prompting. | “Determine the highest qualification attained by the candidate mentioned in the resume, in below format” Example Output Format: (Degree from Institution, completed between Start Year and End Year.) | Example Output Format: **All educational qualifications list of candidate mentioned in resume** (Mention in descending order of years, if years given) - Qualification 1 - Qualification 2 **Which among this list has largest year ** - Qualification with highest year **Highest Educational Qualification in that list** (Complete Degree) from (Institution), completed between (Start Year) and (End Year). |
| MODEL OUTPUT | B.Tech from ABC University between 2011-2014 | MBA from XYZ University, completed between 2014 and 2016. |
| Issue: - Directly asks to identify highest qualification without any steps. - If long list of qualifications given, fails to identify highest in order. | Improved: - Uses chain-of-thought prompting. Model might fail to identify the highest qualification in a huge text. - Step-by-step reasoning helps in taking smaller decisions at a time preventing wrong output due to directly jumping on final decision. | |
| Case III : Prompting to transcription models. | “This is a candidate answering technical interview questions in English. The transcription should be accurate and related to programming concepts, not random noise or background sounds.” | No prompt |
| MODEL OUTPUT | - In case of noise audio, did not transcribe the audio when noise encountered in background. | - Transcribed the audio with noise in between correctly. |
| Issue: - Giving a strict prompt to the transcription model can result in completely skipping the transcription of noisy audio. | Improved: - The prompt in the transcription model helps prevent hallucinations in the transcribed text but does not improve its accuracy, so it shouldn’t be made too strict. |