When using an LLM for data enrichment, giving it a long list of items to extract (e.g., inventory, images, features) results in low-quality output. A more effective method is to run separate, sequential passes for each data point, which improves accuracy and allows you to handle edge cases between steps.
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Large transcript files often hit LLM token limits. Converting them into structured markdown files not only circumvents this issue but also improves the model's analytical accuracy. The structure helps the AI handle the data more effectively than a raw text transcript.
For complex, multi-step AI data pipelines, use a durable execution service like Trigger.dev or Vercel Workflows. This provides automatic retries, failure handling, and monitoring, ensuring your data enrichment processes are robust even when individual services or models fail.
A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.
Instead of one large context file, create a library of small, specific files (e.g., for different products or writing styles). An index file then guides the LLM to load only the relevant documents for a given task, improving accuracy, reducing noise, and allowing for 'lazy' prompting.
Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.
Instead of spending time trying to craft the perfect prompt from scratch, provide a basic one and then ask the AI a simple follow-up: "What do you need from me to improve this prompt?" The AI will then list the specific context and details it requires, turning prompt engineering into a simple Q&A session.
Achieve higher-quality results by using an AI to first generate an outline or plan. Then, refine that plan with follow-up prompts before asking for the final execution. This course-corrects early and avoids wasted time on flawed one-shot outputs, ultimately saving time.
When a prompt yields poor results, use a meta-prompting technique. Feed the failing prompt back to the AI, describe the incorrect output, specify the desired outcome, and explicitly grant it permission to rewrite, add, or delete. The AI will then debug and improve its own instructions.
Instead of creating mock data from scratch, provide an LLM with your existing production data schema as a JSON file. You can then prompt the AI to augment this schema with new fields and realistic data needed to prototype a new feature, seamlessly extending your current data model.
Instead of a single massive prompt, first feed the AI a "context-only" prompt with background information and instruct it not to analyze. Then, provide a second prompt with the analysis task. This two-step process helps the LLM focus and yields more thorough results.