Decagon·Software Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say

May 2026

Summary

Decagon system design round for a software engineer role. The whole thing was one big open-ended question about building a domain-specific chatbot from scratch, covering basically every layer of the stack. Dense but kind of interesting if you're into LLM infra.

Questions Asked (1)

Design a complete end-to-end chatbot system for a single enterprise customer operating within one domain, covering ingestion and indexing of their content, retrieval with vector search and reranking, LLM selection and prompting, conversation memory, tool-use orchestration, safety guardrails, evaluation, and deployment topology.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This is a monster of a question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints (domain, data volume, latency, compliance) to scope the design. Then walk through the architecture end-to-end, covering ingestion, retrieval, LLM orchestration, memory, safety, evaluation, and deployment, while highlighting key trade-offs and justifications. Conclude with a summary of how the components integrate and potential future improvements.

Pro tip: Emphasize the importance of evaluation-driven development: define metrics and build a test harness early to iterate on retrieval and prompting. Also, discuss how you would handle failure modes and ensure safety in a single-tenant enterprise context.

1. Clarify Requirements and Constraints

Ask questions to understand the domain, data sources, volume, latency, compliance, and user expectations. This ensures the design is tailored to the enterprise's needs.

2. Design Ingestion and Indexing Pipeline

Outline how to ingest content (documents, databases, APIs), preprocess (chunking, cleaning), and index for retrieval (vector embeddings, metadata). Consider incremental updates and data freshness.

3. Architect Retrieval and LLM Orchestration

Describe the retrieval flow: vector search, reranking, and LLM selection. Explain prompting strategy, conversation memory management, and tool-use orchestration for actions.

4. Implement Safety, Evaluation, and Deployment

Detail guardrails (input/output moderation, PII redaction), evaluation metrics and pipeline, and deployment topology (cloud vs on-prem, scaling, monitoring).

5. Summarize and Discuss Trade-offs

Recap the end-to-end system, highlight key trade-offs (e.g., latency vs accuracy, cost vs performance), and suggest potential enhancements.

Key Points to Mention

Vector search with embedding models and approximate nearest neighbor algorithms (e.g., HNSW, IVF) for efficient retrieval.
Reranking using cross-encoders or LLM-based scoring to improve relevance of retrieved documents.
LLM selection criteria: domain adaptation, cost, latency, and fine-tuning options; prompting techniques like few-shot and chain-of-thought.
Conversation memory: strategies for maintaining context (e.g., summarization, sliding window) and handling long conversations.
Tool-use orchestration: integrating APIs and functions via frameworks like LangChain or custom orchestrators, with error handling.
Safety guardrails: input/output filtering, PII detection, and compliance with enterprise policies; evaluation metrics like faithfulness, relevance, and toxicity.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.