Prompting like a Pro
Effective prompting is the foundation of getting useful results from any AI-powered feature in GetFocus. Regardless of which feature you're using, one principle applies everywhere:
🗝️ Be clear and specific about your requirements and the output you expect.
LLMs have evolved significantly, and with improved reasoning capabilities, there's no longer a need for overly complex prompts or assigning roles like "You are a market analyst...". Instead, focus on clarity and precision.
Prompting in General Chat
Use General Chat to explore topics, do quick research, or brainstorm ideas. The same clarity principle applies: the more focused your question, the more useful the response.
Technology Scouting
When using the Technology Scouting feature, the way you phrase your prompt has a major impact on the results. If you have access to the Agent, it will guide you through defining your problem statement automatically — you don't need to craft the initial scoping prompt yourself. If you don't have access to the Agent, follow the manual prompting steps below.
The system can categorize a wide range of technologies, from established to emerging and niche, but the specificity of your prompt determines how broad or focused the returned list will be.
Here are two examples to illustrate this:
🔋 Example 1: Battery Technologies
- General prompt: "Energy storage technologies" Returns a broad range of technologies such as lithium-ion batteries, flow batteries, supercapacitors, and mechanical storage systems.
- Specific prompt: "Battery chemistries" Focuses the results on different chemical compositions, such as lithium-sulfur, sodium-ion, or solid-state batteries.
- Highly specific prompt: "Battery chemistry for electric aviation" Narrows the scope even further to technologies optimized for weight, energy density, and safety in aviation use cases.
Each level of specificity results in a different depth and focus of technologies.
🧬 Example 2: Medical Diagnostics
- General prompt: "Medical diagnostic technologies" Returns a wide set of categories including imaging technologies, wearable sensors, biosensors, and lab-on-a-chip devices.
- Highly specific prompt: "Diagnostic technologies for early cancer detection" Targets a narrower set of tools such as liquid biopsies, circulating tumor DNA (ctDNA) analysis, and AI-based imaging solutions.
Again, a more focused prompt will guide the system toward more targeted, relevant technologies.
Once you have a list of technologies, a common next step is down-selection. Before comparing, check whether the technologies are competing or complementary:
- Complementary — work together in the same application (e.g., battery management systems alongside lithium-ion batteries)
- Competing — serve the same purpose in different ways (e.g., lithium-sulfur vs. sodium-ion batteries)
Also compare like with like. Avoid putting a single technology (e.g., solid-state batteries) head-to-head against a broad category (e.g., energy storage technologies) — the category contains dozens of specific solutions, making the comparison misleading.
Useful prompts for structuring comparisons:
By framing prompts with this distinction in mind, you’ll generate more meaningful scouting insights and avoid misleading conclusions.
When it comes to evaluation, there are two main approaches, depending on how much control you want to retain:
✅ Option 1: Give More Control to the LLM
If speed is your priority, you can let the model take the lead. For example:
This approach is fast but gives the LLM broad freedom in how it interprets and structures the evaluation.
✅ Option 2: Stay in Control
If you prefer a more structured and tailored evaluation, include specific instructions in your prompt. For example:
- Define evaluation criteria (e.g., pressure resistance, weight, emissions).
- Request scoring or binary values (e.g., 1–10 scale, or Yes/No).
- Specify the output format for clarity (e.g., a table with scores and reasoning).
This way, you remain in control of the evaluation framework while still leveraging the LLM’s reasoning capabilities.
Write effective Patent Search Queries
Searching in GetFocus is straightforward. There are multiple ways to find relevant patents (read more in Search Types in GetFocus ). However, when searching by technology name, it’s worth giving extra thought to your query.
Patents are legal documents that often cover multiple applications, materials, or chemistries. The depth of your query determines the size and focus of your results.
💡 Broader queries are useful in emerging domains where overly specific language might exclude relevant patents that use different technical terminology.
Let’s take a look at some search query examples:
Very specific: “Hydrometallurgical recycling of lithium-ion batteries for recovery of cobalt and nickel.”
- Very narrow: only patents that match this precise combination will appear.
- Risk: results may be too few, especially in niche or emerging domains.
- Use this approach when you want to quickly check whether a technology is already patented for a specific material, use case, or application.
Specific: “Hydrometallurgical recycling of lithium-ion batteries.”
- Broader than the previous one, retrieving more patents for an overview of relevant inventions.
- Best for building a dataset when you want a full picture of how a technology is applied to a specific use case.
- Works well for established technologies where enough data exists.
Broader: “Hydrometallurgical recycling of batteries.”
- Returns a large and varied set of patents, including some only loosely connected to your original intent.
- Useful for scouting emerging or niche domains, where being too specific might exclude valuable results.
- Advantage: increases the chance of discovering unexpected but relevant insights, since patents often use broad legal and technical language.
With these examples in mind, you can adjust your query detail depending on what your goal is.
Writing LLM filter prompts
Another key area where prompting skills are important is when filtering datasets with the LLM filter. (For an overview of what the LLM filter is and how it works, see the article: “How to filter search results?How to filter search results?”)
Best practices and common mistakes
As with any other prompt, clarity in your instructions is essential. The way you phrase your filter prompt directly impacts the quality and precision of the results.
Even when you aim for clarity, it’s easy to fall into a few common prompting mistakes that can confuse the model or weaken your results.
Common mistakes to avoid:
- Confusing Instructions The prompt mixes multiple ideas or unclear wording, leaving the model uncertain about what to prioritize.
- Vague Instructions The prompt lacks precise terms, definitions, or measurable criteria, leading to broad or inconsistent results.
- Contradictions The prompt includes conflicting requirements (e.g., “include A but not A-like items”), making it impossible for the model to follow a single logic.
- Incomplete Instructions Essential details such as scope, desired format, or evaluation criteria are missing, forcing the model to guess.
- Assuming the LLM can read your mind The prompt relies on implied intent instead of explicitly stating expectations and boundaries.
Best practices for writing effective LLM filter prompts:
- Start with a clear filtering command
- Example: “Find/only include patents/inventions that…”
- Use strong, directive phrasing
- Prefer words like “explicitly,” “specifically,” "primary," “focus on,” or “discuss.”
- Avoid vague verbs like “mention,” since patents often reference technologies without making them the central subject.
- Combine technology and use case requirements
- Example: “The patent must explicitly state both the technology (lithium-sulfur batteries) and the use case (electric aviation).”
- Define what counts as “relevant” and "irrelevant"
- Example: “Include patents that specifically propose a novel technical solution, not just incremental design variations.”
- Use exclusion rules for precision
- Example: “If not all conditions are met, exclude the patent.” This enforces strict filtering and ensures only highly relevant results are returned.
- Example: “Exclude patents focused solely on consumer electronics, unless they directly address energy storage at grid scale.”
LLM filter prompts examples
To make these best practices more concrete, let’s look at a few examples of bad prompt versus well-structured ones.
❌Too vague:
- Likely to return irrelevant results where batteries are only referenced in passing.
✅ Clear and directive:
- Focuses results on patents where lithium-ion batteries are the main subject.
✅ Multi-condition with strict exclusion:
- Includes multiple conditions for precision.
- Uses strict exclusion logic to filter out noise.
❌ Ambiguous scope:
- Uses undefined terms (“some kind of smart control,” “regular ones,” “the other kind”) that make classification ambiguous.
- Mixes conflicting scope instructions (“Include car chargers but not the other kind”) without clarifying category boundaries.
- Lacks measurable criteria (“really quick ones”) that would allow consistent filtering.
- Provides no clear analytical focus (“it’s mostly about how it handles power, or something like that”), leaving interpretation open-ended.
✅ Structured with inclusion/exclusion rules:
- Defines the target concept precisely and includes clear inclusion/exclusion rules.
- Differentiates closely related domains (smart feedback-based chargers vs. simple constant-voltage chargers).
- Adds synonym hints and technical cues (e.g., multi-stage charging, feedback sensors, adaptive control) to stabilize classification and reduce ambiguity.
🧩 Template for LLM Filter Prompt
Writing an effective LLM filter prompt comes down to precision, structure, and clear logic. The best prompts define exactly what to include and exclude, give the model measurable cues to work with, and anticipate edge cases where results could be ambiguous. Use the template below as a practical starting point for building consistent, high-quality LLM filters.
Prompting for Chat with Set and Chat with Invention
Another area where prompting skills are essential is when performing deep dives using Chat with Set (CwS) or Chat with Invention (CwI).
Prompting Tips for Chat with Set
Responses in Chat with Set are drawn from your patent dataset. During longer conversations, the model may also draw on general knowledge — use anchoring phrases to keep results grounded in the data:
- "According to the set…"
- "Based on the patent data…"
Example prompts:
To help you get started, here are a few ideas for prompts you might try in CwS:
⚠️ The model decides on its own what counts as "recent." For precise date ranges, either specify years in your prompt (e.g., "between 2018 and 2023") or apply a publication date filter before starting the chat.
Prompting Tips for Chat with Invention
When it comes to Chat with Invention, the main recommendation remains the same: keep your prompts clear and directive.
Here are some examples to help you get started: