Lifecycle of a Prompt: A Guide to Effective Prompts
Tags: python, llm, promptengineering, llmops
Published: 2023-09-18 12:54:06

## 1. Design & Experiment

The design phase is the first stage of the prompt lifecycle and involves developing the initial prompt or the idea to improve an existing prompt. The design of a new prompt is not simply the prompt you come up with. It's essential to create a prompt that is effective and has good prompt economics. A complete prompt is a combination of the following:

- Prompt text.

- LLM provider and specific model.

- Configuration of hyperparameters.

Any combination of these should be considered a unique prompt and handled throughout the prompt lifecycle. During this phase, one of the pain points is keeping track of all the prompt variations you are considering or developing.

A new prompt or new variation of an existing one can have different goals:

- Adding new features and use cases.

- Adding new LLMs and models for existing use cases.

- Continuous improvement efforts identified in stage 5 of the prompt lifecycle.

- Prompt optimization to reduce costs while maintaining effectiveness.

- Improving user satisfaction, based on collected feedback.

Specific tooling for this stage consists of the following:

- Selecting the right LLM provider and specific model.

- The configuration of hyperparameters that result in high-quality responses

- Estimation of prompt economics.

- A playground to see potential responses allows variation testing.

## 2. Differentiate & Personalize

After designing a prompt, the second stage involves differentiating and personalizing it and its hyperparameters. This stage requires specific tooling for managing variations:

- The granular roll-out of new experiments and versions across different environments.

- Defining rules and conditions for serving specific variants.

- Versioning of prompts and configurations.

- Intuitive variation testing.

## 3. Serve & Operate
Once a new prompt is considered ready for application use, the changes must propagate throughout the DevOps cycle on different non-production environments (development, test, acceptance, production). But, even in production, applications would need customization of prompts. These customizations can potentially be due to the following:

- Roles.

- Personalization.

- Localization.

- Product tiers and subscriptions.

- Canary releases and A/B testing.

Specific tooling required in this phase:

- MLOps tools.

- Low-latency and secure serving of prompts.

- Real-time logging and insights.

- Versioning and rollbacks.

- Customization of prompts and configurations being served in a custom context.

- Tracing and auditing of prompts for compliance.

## 4. Analyze Feedback & Adapt

The fourth stage of the prompt lifecycle involves monitoring and collecting feedback. This stage requires specific tooling such as logs, analytics, and feedback software. Pain points associated with this stage include the need for transparency on which prompt is served in particular contexts and the qualitative and quantitative feedback about its effectiveness. In this case, qualitative feedback is user satisfaction, and quantitative feedback is performance and prompt economics. These data points collected asynchronously must be related to the specific prompt evaluated and served. Additionally, since you're running on production, DevOps tooling is needed to keep control of your tech stack.

Specific tooling required in this phase:

- Quality and satisfaction feedback collection.

- Prompt economics and performance metrics across LLMs and models.

- Kill switches and feature flags.

Finally, the lifecycle closes the continuous improvement cycle and involves analyzing and improving the prompt. Based on the qualitative and quantitative feedback collected, improvements can be hypothesized and (re)designed in phase 1.

Tooling required in this phase:

- Data analysis of served prompt variations and performance over time.

- Dashboards.

- Recommendation agents.

By understanding the prompt lifecycle and the pain points associated with each stage, product teams can at least be aware of potential pain points and tools for remediation.

## The Current State of Tooling Solutions for Prompt Lifecycle Management 

Many prompt engineering practitioners are still in the honeymoon period of excitement and building shallow gadgets. There are no best practices and tooling yet for conducting DevOps for your LLM's prompt-infused applications. This is quite an underserved niche.

A lot of the existing tooling focuses on teams that do more fundamental work, such as:

- Building their own models.

- Training and fine-tuning with custom or proprietary data.

- Store and compute LLM models.

Addressing the Limitations of Current Tooling Solutions 
To summarize, the current gaps we see in tooling for prompt engineering are:

### Prompt lifecycle management.

- No-code collaboration.

- Multi-LLM and multi-modal configurations.

- Versioning.

- Integrated multi-LLM playgrounds.

- Single source of truth preventing fragmentation.

### Business Rules and Remote Configurations

- Context-aware serving of prompt variations.

- Personalization.

- Localization.

### DevOps capabilities

- Kill-switches and feature flags.

- Staged roll-outs and canary releases.

- Enterprise-grade security.

- Traceability and auditing of served prompts.

## Orquesta AI Prompts

We empower product teams to engineer with the Orquesta Cloud suite of building blocks.

Orquesta AI Prompts infuses your existing tech stack with prompt engineering capabilities with a couple of lines of code and enables you to conduct LLM Ops. Get a grip on your prompt lifecycle management and use enterprise-grade tooling.

Read more about [Orquesta AI Prompts](https://orquesta.cloud/platform/llm-ops-prompt-engineering).