Prompt Engineering for Different LLMs: Which Model Understands You Best?

The Prompt Dilemma in Everyday B2B Life

You’ve likely experienced it yourself: The perfect prompt for ChatGPT delivers mediocre results with Claude. What generates precise answers with Perplexity leads only to superficial outputs in Gemini.

This inconsistency costs companies valuable time every day. Your project managers experiment with different phrasings, HR teams get varying quality in job postings, and IT departments struggle with unpredictable documentation results.

The reason doesn’t lie in a lack of AI skills within your teams. Every large language model was developed with different goals, trained on distinct datasets, and follows its own architectural logic.

But what does this mean in concrete terms for your company’s daily operations? Which prompting strategy works best with which model? Most importantly: How can you use these differences deliberately for better business outcomes?

The good news: With the right understanding of model-specific quirks, you can turn this challenge into a competitive advantage.

Why LLMs Respond Differently

Imagine briefing four different consultants for the same project. Each brings different experiences, working methods, and ways of thinking.

It’s exactly the same with LLMs. OpenAI developed GPT-4 as a universal tool for a wide range of tasks. Anthropic designed Claude with a focus on safety and structured reasoning. Perplexity specialized in fact-based research, while Google goes multimodal with Gemini.

These differing design targets are reflected in the training data. ChatGPT learned from an extensive mix of online content, books, and conversations. Claude received additional training for logical reasoning and ethical trade-offs.

Perplexity combines language model capabilities with real-time web search. Gemini was built from the ground up to handle text, code, images, and video.

The transformer architecture provides a shared basis, yet parameter counts, attention mechanisms, and fine-tuning methods differ substantially. What’s considered the “optimal” input for one model can produce suboptimal results in another.

That’s why you need model-specific prompt strategies—there’s no such thing as a universal one-size-fits-all solution.

ChatGPT/GPT-4: The Versatile All-Rounder

ChatGPT is the Swiss Army knife among LLMs. OpenAI developed GPT-4 for maximum versatility—from creative copywriting and analytical tasks to code generation.

This flexibility makes ChatGPT the ideal entry-level tool for organizations. Your teams can achieve productive results instantly, without the need for deep specialist knowledge.

Optimal Prompt Structure for ChatGPT:

ChatGPT responds particularly well to clear role definitions. Start your prompts with “You are a…” or “As an expert in…” to activate specific knowledge areas in the model.

Use a conversational tone. ChatGPT is optimized for dialogue. Ask follow-up questions, request clarifications, or ask for alternative approaches.

Example of a business prompt:

“You are an experienced sales manager in mechanical engineering. Create a structured quote for a custom metalworking machine. Budget: 250,000 euros. Target audience: automotive suppliers. Please include technical details, lead time, and service package.”

ChatGPT handles even complex tasks reliably if you provide context step by step. Start by feeding it the background information before giving it the main task.

Weaknesses of ChatGPT:

Current information remains a problem. GPT-4 doesn’t know about events after its training cutoff. It’s not suitable for research requiring up-to-date information.

Sometimes, the model tends to “hallucinate”—it invents plausible-sounding facts. Always verify critical information against trustworthy sources.

Other models are recommended for highly precise, fact-based tasks. ChatGPT excels in creative, communicative, and strategic challenges.

Claude: The Structured Analyst

Anthropic developed Claude with a clear focus: safety, transparency, and systematic reasoning. This makes Claude the perfect partner for complex analytical projects and sensitive corporate data.

Claude likes to think in steps. Where ChatGPT may jump directly to an answer, Claude shows its thought process. This builds trust and transparency—vital factors in the B2B context.

Optimal Prompt Strategy for Claude:

Structure your prompts hierarchically. Claude processes complex, multi-part requests especially well if you use clear bullet points or sections.

Request an explicitly step-by-step approach. Phrasings like “Analyze systematically…” or “Proceed step by step…” trigger Claude’s strengths.

Example of a strategic Claude prompt:

“Systematically analyze the market launch of our new SaaS product. Include: 1) target group segmentation, 2) pricing strategies, 3) go-to-market channels, 4) competitive landscape, 5) risk evaluation. Weigh each factor and derive concrete recommendations for action.”

Claude responds exceptionally well to detailed context. The more precisely you describe your company, industry, and challenges, the better the answers will be.

Claude’s Unique Strengths:

When it comes to ethical dilemmas and compliance topics, Claude demonstrates outstanding expertise. The model was specifically trained for responsible AI usage.

For document analysis and processing, Claude often delivers more precise results than ChatGPT. Its ability to digest long documents and summarize them in an organized way is impressive.

Claude is excellent for strategic planning. The model can simulate various scenarios and systematically assess their implications.

Limitations of Claude:

Claude can be a little “deliberate” for quick, spontaneous brainstorming sessions. Its systematic approach takes time, which can be a hindrance for creative processes.

For highly technical coding tasks, ChatGPT often provides more pragmatic solutions. Claude tends to offer elaborate explanations, even for simple programming challenges.

Perplexity: The Fact-Based Researcher

Perplexity solves a fundamental problem of most LLMs: lack of current information. By combining language model capabilities with real-time web search, Perplexity consistently delivers up-to-date, source-based answers.

For businesses, this means market analyses, competitive intelligence, and trend research can finally run without manual post-processing.

Prompt Optimization for Perplexity:

Phrase your queries like research assignments. Perplexity shines at specific fact-finding missions, not at creative or strategic tasks.

Specify concrete time frames and geographic contexts. The more precise your parameters, the more relevant the results.

Example of a Perplexity prompt:

“Which German SaaS companies received Series A financing of more than 10 million euros between January and November 2024? Sort by funding amount and name the lead investors.”

Perplexity responds excellently to follow-up questions. Use the conversation feature to dive deeper into a topic step by step.

Perplexity’s Core Competencies:

Perplexity is unbeatable for market research. The tool delivers up-to-date figures, trends, and developments with direct source references.

Competitive intelligence works exceptionally well. You’ll quickly get overviews of competitor activity, product launches, or strategy shifts.

News monitoring and trend analysis are among Perplexity’s standout disciplines. Your teams can stay current on industry developments—without time-consuming manual research.

Limitations of Perplexity:

Perplexity is less suitable for creative or strategic planning tasks. The tool is focused on fact retrieval, not idea generation.

Its quality depends heavily on available online sources. For highly specific B2B niches, the data base can become thin.

Perplexity, by its nature, cannot incorporate your internal company data. For analyzing company-specific information, you’ll need other tools.

Gemini: The Multimodal Specialist

Google developed Gemini as the first natively multimodal approach. Text, images, code, and videos are processed simultaneously—a decisive advantage for modern business processes.

Your marketing teams can optimize campaign visuals and text side by side. Technical documentation with screenshots is fully analyzed. Presentations can be assessed holistically.

Gemini-Specific Prompt Strategies:

Leverage Gemini’s multimodal strength intentionally. Combine textual instructions with visual inputs for more precise results.

Gemini handles context switching between different media types very well. In a single prompt, you can switch between text analysis and image interpretation.

Example of a multimodal Gemini prompt:

“Analyze our new product brochure [PDF upload]. Assess both textual clarity and design elements. Provide concrete suggestions for improvement for the target group ‘Technical Procurement Managers in Mid-Sized Companies’.”

Google’s deep learning expertise is evident in Gemini’s code understanding. For software development and technical documentation, Gemini often delivers highly accurate results.

Gemini’s Strengths in Detail:

Presentation optimization is a strong suit. Gemini can assess slide decks holistically and suggest concrete improvements to both design and content.

For technical documentation with visual elements, Gemini is the top pick. Screenshots, diagrams, and text are contextualized together.

Video content analysis opens up new possibilities. Training videos, webinars, or product demos can be automatically transcribed and analyzed.

Where Gemini Shows Weaknesses:

For purely text-based tasks without visual elements, Gemini rarely offers advantages over ChatGPT or Claude.

Integration into existing workflows is sometimes more complex, as multimodal capabilities require specialized interfaces.

For highly sensitive company data, Google products often come with stricter compliance requirements than specialized B2B providers.

Practical Prompt Strategies in Direct Comparison

Theory is one thing—practice is another. Here’s how the same business task can be optimally formulated for different LLMs.

Task: Creating a Job Posting for an AI Project Manager

ChatGPT-Optimized Prompt:

“You are an experienced HR director in an innovative mid-sized enterprise. Write an appealing job posting for an AI project manager. Target audience: tech-savvy professionals with 3–5 years of experience. Style: modern but professional. Emphasize work-life balance and development opportunities.”

Claude-Optimized Prompt:

“Systematically develop a job posting for an AI project manager. Consider: 1) requirements profile (technical/professional), 2) areas of responsibility, 3) benefits and career prospects, 4) company culture, 5) application process. Target group: experienced tech professionals. From each component, derive concrete formulation suggestions.”

Perplexity would be unsuitable here—job postings require creativity, not up-to-date web research.

Task: Competitive Intelligence on a New Market Competitor

Perplexity-Optimized Prompt:

“Analyze the German company [Competitor XY] for the period 2023–2024. Focus: product portfolio, pricing strategy, market positioning, key personnel, funding, media coverage. Sort results by relevance and recency.”

ChatGPT would be limited here due to lack of current data.

Universal Prompt Principles across All Models:

Specificity beats generality. “Create a marketing strategy” leads to generic answers. “Develop a B2B LinkedIn campaign for engineering decision-makers with a €15,000 budget over 3 months” yields actionable results.

Explicitly define your role and the model’s role. “As the CEO of a 150-person company, I need…” and “You are an experienced consultant for…” provide necessary context.

Specify output formats. “Structure the answer as a table with…” or “Break down into 3 main points, each with subpoints…” leads to more usable results.

Iteration is key. No prompt works perfectly on the first attempt. Refine step by step and incorporate successful phrasings into your standard templates.

B2B Implementation: From Testing to Productive Use

The best prompt strategy is useless without a structured rollout. Here’s the proven Brixon approach for sustainable AI integration.

Phase 1: Pilot Tests (4–6 weeks)

Start with 3–5 concrete use cases from your daily business. Choose tasks with high frequency and clear quality criteria.

Test each use case with 2–3 different models. Systematically document prompt variants and result quality.

Example from engineering: technical documentation, quote texts, and service instructions are ideal for first trials.

Phase 2: Team Training (2–3 weeks)

Train your staff on the most successful prompt patterns. But beware: copy-paste templates don’t cut it. Your teams must understand principles to apply them flexibly.

Jointly develop template libraries for recurring tasks. These templates become valuable company assets.

Establish feedback loops. Successful prompt variants should be documented and shared.

Phase 3: Scaling (ongoing)

Integrate AI tools into existing workflows rather than creating separate processes. Seamless integration determines adoption and ROI.

Measure tangible productivity gains. Time saved, quality improvements, and cost reductions must be quantifiable.

Develop internal power users to act as multipliers. These AI champions drive advancement and support colleagues facing challenges.

Governance and Quality Assurance:

Set clear guidelines for AI use. What data can be processed? Which tasks require human review?

Implement review processes for critical outputs. AI speeds up workflows, but doesn’t replace professional quality control.

Plan regular tool evaluations. The AI market evolves rapidly—new models can quickly overtake existing solutions.

The key is a systematic approach. Companies that succeed with AI start small, learn fast, and scale thoughtfully. Brixon supports you with proven methods and measurable results.

Frequently Asked Questions