Prompt Engineering: Guide AI authoritatively

Often, AI disappoints because of suboptimal commands and configuration:

AI responds generically to specific queries
Gives fictitious answers when it can't find the data it requires
Confuses terms, provides info that is out-of-date or omits important details

That's why we apply professional prompt engineering techniques:

Preliminary analysis:

We assess objectives and data
We define roles, tasks, rules, tone and format

Technical optimization:

We optimise behavioral parameters
Iterative testing and refinement until satisfactory results are achieved

Advanced programming:

We show the AI examples relevant to your corporate environment
We structure reasoning step-by-step
We configure restrictions to avoid critical errors

Context engineering: Prepare the ground

This is the art and science of selecting which information the AI model will consider when generating an answer. AI agents, like people, have limited working memory that must be reserved for the right data in the right format; this is why we take care of the following aspects:

Selection:

Not every document deserves a place in the knowledge base. We exclude low-quality documents, duplicates, drafts and similar

Organisation:

Documents must be labelled, segmented and structured so the AI model can prioritise them correctly

Updates & version control:

Out-of-date data and multiple versions of the same file lead to avoidable hallucinations and errors

Format optimisation:

When the AI can read the data more easily and accurately, output becomes far more reliable

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