Ensuring Accuracy in AI-Generated Content: A Case Study with LinkedIn Posts

Introduction

In the devlog-ist/landing project, which focuses on creating engaging landing pages, we encountered an interesting challenge: ensuring the accuracy of AI-generated content, specifically for LinkedIn posts. The issue arose when the AI model, while drafting the post text, rephrased the job position title, leading to inconsistencies with the banner image associated with the post. This post explores how we addressed this problem by refining the prompt to enforce precise content.

The Problem: Inconsistent Information

The initial prompt gave the AI model considerable freedom in rephrasing the job title. While this flexibility aimed to produce more natural-sounding text, it inadvertently caused mismatches. For example, if the intended title was "Senior Software Engineer," the AI might rephrase it as "Experienced Software Developer," creating a discrepancy.

The Solution: Precise Prompting

To resolve this, we modified the prompt to inject the literal value of the current position and explicitly instructed the model not to substitute it. This approach ensured that the AI used the exact title in the post text, eliminating any ambiguity or inconsistencies.

Consider this illustrative example:

Original Prompt: Write a LinkedIn post about a software engineer role.
AI Output: Exciting opportunity for an experienced developer.

Modified Prompt: Write a LinkedIn post about a role as [current_position: Senior Software Engineer]. Do not rephrase the position title.
AI Output: We're hiring a Senior Software Engineer!

The modified prompt enforces accuracy by providing the exact term and restricting the AI from altering it.

Results

By implementing a more precise prompt, we significantly improved the accuracy and reliability of the AI-generated LinkedIn posts. This change ensured that the text aligned perfectly with the visual elements, providing a consistent and professional message.

Next Steps

Future improvements could involve adding further constraints to the prompt to control other aspects of the generated text, such as tone and style, while maintaining accuracy. Additionally, we could explore techniques for validating the AI's output against a predefined schema to catch any remaining inconsistencies.