n8n Feature Improvement Ideas Using External Research

Harshal
Sep 28, 2025
3 min read

Solution Ideation Deep-Dive Example For PM Interviews

Here, I show a deep dive into a product memo example I did for n8n a few months ago, when I interviewed with them. Fast-forward, I got a job offer and will join them in Q4 2025. Since this doesn’t have n8n employee inputs, the information is not accurate, but I hope it gives you ideas on the approach.

In earlier posts, I shared a framework to write a case study or product memo when you interview for Product Management roles with companies.

Based on my n8n user interviews, community discussion analysis, competitive analysis, and trying out n8n and competitor products, I suggested some feature improvements.

I grouped them into themes and wrote them in descending priority order here.

Some might’ve been implemented by the time you read this, so these opportunities may not exist anymore. My goal here is to show you a deep-dive example for your Product Management interviews.

explore 5 themes for user needs in this post.

Identifying User Needs And Gaps

Before I ideated on solutions, I identified user needs and gaps. To identify user needs and gaps, I:

1 - Builder Confidence & Iteration Speed

I kept this as the highest-priority theme.

These features directly increase trust, reduce time-to-value, and improve workflow quality — core to n8n’s visual builder advantage.

Prompt playground, versioning, diffing. Visual diff for prompt changes – aids clarity in collaborative builds.
model evaluation framework - exists but include in templates to show recommended way to use easily
prompt/model suggestions – reduces guesswork and speeds up building.
Auto-eval report after runs – reinforces feedback loop and quality checks.
AI assistant that searches external docs (e.g., Pinecone) – nice-to-have for non-technical users because right now I need 2 AI searches for any integration problem.

2 - Production-Readiness & Reliability

Crucial for enterprise users and anyone deploying n8n beyond prototyping.

Cost & latency tracking per node – helps users make sustainable infrastructure choices.
Fallbacks for failed model/tool calls – improves reliability in real-world conditions.
Out-of-the-box infra monitoring & alerts – enables users to operate with confidence.
Catch model drift (not just workflow failure) – adds long-term robustness.
Inference engine metrics (production-grade reliability) – transparency into performance.

3 - Integration Flexibility & Model Access

Important for unlocking advanced use cases and platform stickiness through orchestration.

model router node – streamlines provider choice across workflows. Claude, Gemini, Grok node support – broadens reach and avoids OpenAI lock-in.
Model selection from Hugging Face (is there) or openRouter – appeals to open-source and experimental users.
Expose more tuning parameters per model – empowers advanced users.
Fine-tuning support via data collation – enables deeper personalization. N8n flows to collate data or RLHF loops.
Deploy models from Hugging Face (inference support) – bridges build and run phases. (is there) - but make it serverless.

4 - Memory, Agents & Context

Schema-aware AI Agent node – reduces bugs in tool invocation and planning.Short-term memory strategies for long conversations – optimizes context handling in chat workflows.
Workflow intelligence to decide when to choose CPUs or GPUs. intelligence to cache some aspects to speed up AI usage and reduce costs.

5 - Developer Experience & Usability

Good for delight and completeness, but not core to growth or trust today.

Whisper native node - valuable but niche use case.
Templates based on ChatGPT use-cases from Harvard case study – great onboarding tool
Collaborative editing co-edit the same workflow
Auto-attach Chat Model sub-node to AI Agent – prevents config errors and improves success rate.

Rest Of The Product Memo

You can read the rest of the product memo here.