按 Quick 组件拆解提示工程：模式与常见陷阱

在本系列的第 1 部分中，我们介绍了 Amazon Quick 中提示工程的基础原则：具体性、上下文设定、少样本示例，以及用于复杂请求的 CRISPE 框架。这些原则具有普遍适用性。本文将逐一介绍各个组件，说明每项 Quick 能力如何以不同方式解读提示，以及哪些模式能为每项能力带来最佳结果。

你可能使用 Amazon Quick Research 进行市场分析，使用 Amazon Quick Flows 实现自动化，使用 Amazon Quick Sight 进行数据可视化，使用聊天 Agent 访问团队知识，或使用操作集成执行跨系统工作流。无论你的目标是什么，以下技巧都能帮助你从通用输出转向精确、可执行的结果。

Quick Research

从 Amazon Quick Research 获得有价值的输出，取决于你如何界定研究目标。该 Agent 会接收你的目标，将其拆分为子主题，在企业数据和外部来源中搜索，然后交付带有引用的结构化报告。模糊的目标会产生浅层报告；具体的目标则会产出可供行动的信息。

明确目标、受众和重点领域

AWS 文档对此有直接说明：“请明确说明你想实现什么、面向谁以及为什么。”一个强有力的目标应说明主题、限定时间范围、识别受众，并告知 Agent 哪些输出最为重要。

示例：

Analyze the adoption of generative AI across US hospital systems over 
the past 12 months. Zero in on two areas: clinical decision support tools 
and administrative automation (scheduling, billing, records management). 
Write the findings for a healthcare-IT executive audience deciding where to invest next.

这一目标告诉 Agent 要调查什么、应采用何种视角，以及读者关心什么。Quick Research 利用这些上下文来制定子问题、选择相关数据源，并围绕你的实际需求组织报告。在撰写研究目标前，请先问自己：

• 这项研究将为哪项决策提供依据？
• 谁会阅读它？
• 什么结果会让他们说“这正是我需要的”？

将复杂主题拆分为子问题

Quick Research 会自动将目标拆解为子主题，但如果你亲自完成部分拆解工作，通常能得到更好的结果。对于多维度研究，请列出你希望得到解答的具体问题。这能为 Agent 提供更清晰的方向，并减少它偏离主题的可能性。

限定来源范围并审核计划

Quick Research 可通过 Quick Index 获取企业数据，并使用 200 多家可信新闻媒体，以及来自 S&P Global、FactSet、IDC、美国专利数据和 PubMed 的高级数据集。输入目标后，请选择要纳入的来源，并审核研究计划草案。竞争分析不需要 PubMed，临床文献综述也不需要新闻文章。缩小来源集合可以让 Agent 更聚焦，并减少噪声。

Quick Flows

一个只能为团队节省五分钟的工作流，与一个能节省五小时的工作流之间的差异，往往取决于你如何编写提示。Quick Flows 可将自然语言描述转化为自动化工作流，但你提供的具体程度、结构和上下文会直接决定返回结果。

明确说明做什么、何时做、在哪里做

模糊的提示会产生模糊的流程。最常见的错误是只描述想要什么，却没有说明如何做、何时做或为谁执行。

之前：“从我们的销售数据创建一份报告。”

之后：“每周一上午 8 点，从 CRM 提取上一周的销售数据，计算总收入和按销量排序的前 10 个产品，生成一页 PDF 摘要，并通过电子邮件发送给销售经理邮件分发列表。”

第二个提示为 Quick Flows 提供了具体锚点：执行时间表、数据源、明确计算项、输出格式和交付对象。每一项细节都会映射到最终流程中的一个步骤。

在定义触发条件时，请同时明确时间安排和条件。“每周一上午 8 点”很清晰；“当新数据到达时”则很模糊。请说明哪个系统产生触发器、什么情况构成“新数据”，以及触发器触发但没有新数据时应如何处理。

将复杂逻辑组织为编号步骤

对于包含两三个以上操作的工作流，请将提示写成编号序列。Quick Flows 支持条件分支、重复循环和用户输入，编号步骤能够自然映射到其内部步骤结构：

示例：

Build a flow that does the following:
1. Accept an uploaded expense report (PDF or image) as input.
2. Extract the total amount, vendor name, date, and expense category from the document.
3. If the total exceeds $500, route it to the finance team’s Slack channel for approval.
4. Once approved, log the expense in our Google Sheets tracker with all extracted fields 
plus the approval timestamp.

这种结构让调试变得直接：如果第 3 步无法正常运行，你会明确知道该从哪里排查。

通过对话迭代，而非重写

借助 Quick Flows 的 Agent 运行时环境，你可以在初始构建后通过对话细化流程。与其重写整个提示，不如使用有针对性的后续指令：

示例：

Add a step between steps 2 and 3 that checks whether the data source returned 
any results. If the query comes back empty, send me a notification instead of 
continuing with the rest of the flow.

Quick Flows 会保留原始提示中的上下文，因此调整可在现有内容基础上进行。

Quick Sight

借助 Amazon Quick Sight，你可以通过对话式查询探索数据。请构建你的分析请求，明确业务问题、相关维度、所需计算和可视化偏好。

分析查询的结构

每个有效的 Quick Sight 查询都包括以下核心元素：

• 业务问题（你要寻求的决策或洞察）。
• 指标（你希望分析的量化度量）。
• 维度（如何对数据进行分组、筛选或细分）。
• 时间段（相关的时间范围）。
• 可视化类型（最能传达洞察的图表格式）。
• 附加分析（趋势线、阈值、统计标记或对比期间）。

遗漏其中任何一项都会迫使 Quick Sight 进行猜测，往往会生成技术上正确但无实际帮助的可视化结果。

分析模式

时间序列：“以堆叠折线图展示过去 12 个月按优先级划分的客户支持工单量。为总量添加趋势线，以便我了解总体工作负荷是在上升还是下降。”

比较：“比较 2025 年第四季度按行业垂直领域和销售代表划分的平均交易规模。以按交易规模降序排列的分组条形图展示，并突出显示平均值低于 5 万美元的任何垂直领域。”

关系：“构建客户生命周期价值与产品使用频率的散点图。按订阅层级为点着色，并添加回归线，以便我了解使用情况对 LTV 的预测力有多强。”

分布：“生成过去六个月项目完成时间的直方图。使用两周一个分箱，将目标完成时间叠加为垂直参考线，并标注项目占比超过 20% 的任何分箱。”

地理：“展示北美地区客户分布的热力图。使用颜色强度表示收入集中度，并为贡献超过总收入 10% 的任何区域添加数据标签。”

优化可视化并创建计算字段

使用 Topics（为对话式查询优化的精选数据集合）时，应围绕业务结果而不是数据操作机制来构建问题。不要问“给我展示用户数表格”，而应问“哪些客户细分群体对我们的移动应用参与度最高，以日活跃用户数和会话时长衡量？”

生成初始可视化后，可通过对话式追问进一步优化：更改图表类型、添加筛选器、修改聚合方式，或请求计算字段。使用自然语言描述公式：

示例：

“添加一个名为‘Customer Health Score’的计算字段，该字段融合续约概率（权重 40%）、产品使用趋势（权重 35%）和支持工单频率（权重 25%，反向计算——工单越少代表健康度越高）。将其显示为 0–100 的指数。”

Quick 聊天 Agent

Amazon Quick 聊天 Agent 将对话式 AI 引入你的组织。你可以控制 Agent 的身份、将其连接至公司的知识库、设置行为护栏，并将其发布给团队。体验质量取决于你配置 Agent 时的周密程度。

定义清晰的 Agent 身份

Builder Mode 中的身份字段为每次响应奠定基础。一个强有力的身份定义应说明角色、界定专业能力，并划清明确边界：

示例：

You are a Cloud Cost Optimization Advisor for our FinOps team. Your expertise 
covers AWS cost management, reserved-instance planning, savings plans analysis, 
and resource right-sizing. Respond only to questions about cloud cost optimization. 
If a question falls outside that scope, say so and suggest the right team to contact 
instead.

如果没有这类边界，Agent 往往会对其专业范围外的问题给出看似自信但不可靠的回答。

随着团队需求演变，请定期审查 Agent 的身份定义。六个月前编写的身份定义，可能已无法反映当前优先事项、工具或组织结构。

连接恰当的知识并设置护栏

将 Quick Spaces 关联到 Agent，决定了它能够获取哪些信息。请审慎操作：一个连接到组织中所有 Quick Space 的 Agent 会呈现无关内容。应让知识源与 Agent 已定义的角色相匹配。

同样重要的是，明确告诉 Agent 在没有答案时应如何处理。如果没有明确指令，Agent 会用貌似合理但实际虚构的回答填补空白：

示例：

When you lack enough information to answer confidently, respond with:
‘I don’t have that in my current knowledge base. Please reach out to [relevant team] 
or check [specific resource] for the latest guidance.’ Never guess or fabricate 
an answer.

请定期审查已连接的 Quick Spaces。过时文档比没有文档更糟，因为 Agent 会自信地引用它。随着团队知识演变，移除陈旧来源并添加新来源。

设计能培养良好习惯的建议提示

建议提示是用户最先看到的内容。它们设定预期，并示范 Agent 最擅长处理的具体化程度。诸如“问我有关成本的问题”这类模糊建议，会教用户编写模糊提示。具体的建议则能培养更好的习惯：

示例：

“在过去 30 天内，我们生产账户中的哪些 EC2 实例系列利用率最低？如果对它们进行规格调整，可以节省多少成本？”

示例：

“比较将我们的按需 RDS 用量切换为 1 年期预留计划与 3 年期 Savings Plans 的成本影响。哪种方案能在节省与灵活性之间取得最佳平衡？”

操作集成

通过操作连接器，Quick 可与 Jira、Slack、Confluence 和 Salesforce 等外部系统进行交互。有效的操作提示应包括清晰意图、所有必要参数和正确的执行顺序。

参数规范

请在一开始就提供所有必需信息。不完整的参数会迫使系统提出后续问题或作出假设：

示例：

Create a Jira issue with these details:
Project: CUSTOMER-SUPPORT
Issue Type: Bug
Summary: ‘[Customer Name] — [Brief Description]’
Priority: High
Assignee: the on-call support engineer
Labels: customer-reported, needs-triage
Description: Include the reproduction steps, expected vs. actual behavior, 
and the customer’s account tier.

操作排序

请以清晰的依赖关系组织多步骤工作流。对步骤编号、指定条件，并明确哪个步骤的输出将提供给下一步：

示例：

Run these steps in order:
1. Search the ‘Product Documentation’ Confluence space for existing pages on [topic].
2. If a page exists, update it with the new content and add a revision note.
3. If no page exists, create one using our standard template.
4. Post a summary of what changed (or what was created) to the #product-docs Slack 
channel with a link.

操作审核处理

对于具有破坏性或批量性质的操作，应构建提示以支持执行前审核步骤。先要求系统生成摘要报告，再仅在你明确批准后执行操作。对于会修改工单、删除记录或向外部利益相关者发送通信的操作，这一点尤其重要。应将审核步骤直接构建到提示中，而不是仅依赖系统级护栏。

应避免的常见陷阱

了解常见错误，有助于你避免在所有 Quick 组件中遇到挫折。

通用陷阱

• 允许多种解释的模糊语言。
• 在单个提示中塞入过多要求。
• 假设 AI 拥有它并不具备的上下文。
• 忽略指定输出格式。
• 未使用真实的边缘案例进行测试。
• 未记录成功模式以供复用。

组件特有的陷阱

**Quick Research：**将搜索查询而非研究目标写入提示。遗漏受众上下文。跳过研究计划审核。

**Quick Flows：**在单个提示中塞入过多操作。跳过用户输入定义。忽略

In Part 1 of this series, we covered the foundational principles of prompt engineering in Amazon Quick: specificity, context-setting, few-shot examples, and the CRISPE framework for complex requests. Those principles apply universally. In this post, we go component by component, showing you how each Quick capability interprets prompts differently and what patterns get the best results from each one.

You might use Amazon Quick Research for market analysis, Amazon Quick Flows for automation, Amazon Quick Sight for data visualization, chat agents for team knowledge access, or action integrations for cross-system workflows. Whatever your goal, the following techniques will help you move from generic outputs to precise, actionable results.

Quick Research

Getting useful output from Amazon Quick Research depends on how you frame the research objective. The agent takes your objective, breaks it into sub-topics, searches across enterprise data and external sources, then delivers a structured report with citations. A vague objective produces a shallow report. A specific one produces something you can act on.

Define the goal, the audience, and the focus areas

The AWS documentation says it directly: “Be specific by stating what you want to achieve, for whom, and why.” A strong objective names the topic, scopes the timeframe, identifies the audience, and tells the agent what outputs matter most.

Example:

Analyze the adoption of generative AI across US hospital systems over 
the past 12 months. Zero in on two areas: clinical decision support tools 
and administrative automation (scheduling, billing, records management). 
Write the findings for a healthcare-IT executive audience deciding where to invest next.

This objective tells the agent what to investigate, what lens to apply, and what the reader cares about. Quick Research uses this context to formulate sub-questions, select relevant data sources, and structure the report around your actual needs. Before writing your objective, ask yourself:

• What decision will this research inform?
• Who will read it?
• What would make them say “this is exactly what I needed”?

Decompose complex topics into sub-questions

Quick Research automatically breaks objectives into sub-topics, but you’ll get better results by doing some of that work yourself. For multifaceted research, list the specific questions you want answered. This gives the agent clearer direction and reduces the chance of it pursuing tangents.

Scope your sources and review the plan

Quick Research draws from enterprise data through Quick Index, 200+ trusted news outlets, and premium datasets from S&P Global, FactSet, IDC, US Patent data, and PubMed. After entering your objective, select which sources to include and review the draft research plan. A competitive analysis doesn’t need PubMed. A clinical literature review doesn’t need news articles. Narrowing the source set focuses the agent and reduces noise.

Quick Flows

The difference between a workflow that saves your team five minutes and one that saves five hours often comes down to how you write the prompt. Quick Flows transforms plain-language descriptions into automated workflows, but the specificity, structure, and context you provide directly shape what you get back.

Be specific about the what, when, and where

Vague prompts produce vague flows. The most common mistake is describing what you want without specifying how, when, or for whom.

Before: “Create a report from our sales data.”

After: “Every Monday at 8 AM, pull the previous week’s sales data from the CRM, calculate total revenue and top 10 products by units sold, generate a one-page PDF summary, and email it to the sales-managers distribution list.”

The second prompt gives Quick Flows concrete anchors: a schedule, a data source, specific calculations, an output format, and a delivery target. Each detail maps to a step in the resulting flow.

When defining triggers, be explicit about both the schedule and the conditions. “Every Monday at 8 AM” is clear. “When new data arrives” is vague. Specify what system generates the trigger, what constitutes “new data,” and what should happen if the trigger fires but no new data exists.

Structure complex logic as numbered steps

For workflows with more than two or three operations, write your prompt as a numbered sequence. Quick Flows supports conditional branching, repeat loops, and user inputs, and numbered steps map naturally to its internal step structure:

Example:

Build a flow that does the following:
1. Accept an uploaded expense report (PDF or image) as input.
2. Extract the total amount, vendor name, date, and expense category from the document.
3. If the total exceeds $500, route it to the finance team’s Slack channel for approval.
4. Once approved, log the expense in our Google Sheets tracker with all extracted fields 
plus the approval timestamp.

This structure makes debugging straightforward: if step 3 isn’t working, you know exactly where to look.

Iterate through conversation, not rewrites

With the Quick Flows agentic runtime, you can refine flows through conversation after the initial build. Instead of rewriting your entire prompt, use targeted follow-up instructions:

Example:

Add a step between steps 2 and 3 that checks whether the data source returned 
any results. If the query comes back empty, send me a notification instead of 
continuing with the rest of the flow.

Quick Flows retains context from the original prompt, so adjustments build on what’s already there.

Quick Sight

With Amazon Quick Sight, you can explore data through conversational queries. Structure your analytics requests to specify the business question, relevant dimensions, required calculations, and visualization preferences.

Query structure for analytics

Every effective Quick Sight query includes these core elements:

• A business question (what decision or insight you’re seeking).
• Metrics (the quantitative measures you want to analyze).
• Dimensions (how to group, filter, or segment the data).
• Time period (the relevant timeframe).
• Visualization type (the chart format that best communicates the insight).
• Additional analysis (trendlines, thresholds, statistical markers, or comparison periods).

Omitting any of these forces Quick Sight to guess, which often produces a technically correct but unhelpful visualization.

Analysis patterns

Time series: “Plot customer-support ticket volume by priority level over the past 12 months as a stacked line chart. Add a trend line for total volume so I can see whether the overall load is rising or falling.”

Comparative: “Compare average deal size by industry vertical and sales rep for Q4 2025. Show it as a grouped bar chart sorted by deal size descending, and highlight any vertical where our average is below $50K.”

Relationship: “Build a scatter plot of customer lifetime value versus product usage frequency. Color-code the dots by subscription tier and add a regression line so I can see how strongly usage predicts LTV.”

Distribution: “Generate a histogram of project completion times over the last six months. Use two-week bins, overlay the target completion time as a vertical reference line, and call out any bin where more than 20% of projects landed.”

Geographical: “Show a heat map of customer distribution across North America. Use color intensity to represent revenue concentration and add data labels for any region contributing more than 10% of total revenue.”

Refining visuals and creating calculated fields

When working with Topics (curated data collections optimized for conversational queries), frame your questions around business outcomes rather than data mechanics. Instead of “show me a table of user counts,” ask “Which customer segments have the highest engagement with our mobile app, measured by daily active users and session duration?”

After generating an initial visualization, refine through conversational follow-ups: change chart types, add filters, modify aggregations, or request calculated fields. Use natural language for formulas:

Example:

“Add a calculated field called ‘Customer Health Score’ that blends renewal probability (40% weight), product usage trend (35% weight), and support ticket frequency (25% weight, inverse — fewer tickets means healthier). Show it as a 0–100 index.”

Quick chat agents

Amazon Quick chat agents bring conversational AI into your organization. You control the agent’s identity, connect it to your company’s knowledge bases, set behavioral guardrails, and publish it to your team. The quality of the experience depends on how thoughtfully you configure the agent.

Define a clear agent identity

The identity field in Builder Mode sets the foundation for every response. A strong identity names the role, defines expertise, and draws clear boundaries:

Example:

You are a Cloud Cost Optimization Advisor for our FinOps team. Your expertise 
covers AWS cost management, reserved-instance planning, savings plans analysis, 
and resource right-sizing. Respond only to questions about cloud cost optimization. 
If a question falls outside that scope, say so and suggest the right team to contact 
instead.

Without boundaries like these, agents tend to answer questions outside their expertise with confident but unreliable responses.

Review your agent’s identity periodically as your team’s needs evolve. An identity written six months ago may no longer reflect current priorities, tools, or organizational structure.

Connect the right knowledge and set guardrails

Linking Quick Spaces to your agent determines what information it can draw from. Be deliberate: an agent connected to every Quick Space in your organization will surface irrelevant content. Match knowledge sources to the agent’s defined role.

Equally important is telling the agent what to do when it doesn’t have an answer. Without explicit instructions, agents fill gaps with plausible but fabricated responses:

Example:

When you lack enough information to answer confidently, respond with:
‘I don’t have that in my current knowledge base. Please reach out to [relevant team] 
or check [specific resource] for the latest guidance.’ Never guess or fabricate 
an answer.

Review connected Quick Spaces periodically. Outdated documentation is worse than no documentation because the agent will cite it with confidence. Remove stale sources and add new ones as your team’s knowledge evolves.

Design suggested prompts that teach good habits

Suggested prompts are the first thing users see. They set expectations and model the specificity the agent performs best with. Vague suggestions like “Ask me about costs” teach users to write vague prompts. Specific ones teach better habits:

Example:

“Which EC2 instance families in our production account have the lowest utilization over the past 30 days, and what would we save by right-sizing them?”

Example:

“Compare the cost impact of switching our on-demand RDS usage to a 1-year reserved plan versus a 3-year savings plan. Which option gives us the best balance of savings and flexibility?”

Action integrations

With action connectors, Quick can interact with external systems like Jira, Slack, Confluence, and Salesforce. Effective action prompts include clear intent, all necessary parameters, and proper sequencing.

Parameter specification

Provide all required information upfront. Incomplete parameters force the system to ask follow-up questions or make assumptions:

Example:

Create a Jira issue with these details:
Project: CUSTOMER-SUPPORT
Issue Type: Bug
Summary: ‘[Customer Name] — [Brief Description]’
Priority: High
Assignee: the on-call support engineer
Labels: customer-reported, needs-triage
Description: Include the reproduction steps, expected vs. actual behavior, 
and the customer’s account tier.

Action sequencing

Structure multi-step workflows with clear dependencies. Number the steps, specify conditions, and make it explicit which step’s output feeds into the next:

Example:

Run these steps in order:
1. Search the ‘Product Documentation’ Confluence space for existing pages on [topic].
2. If a page exists, update it with the new content and add a revision note.
3. If no page exists, create one using our standard template.
4. Post a summary of what changed (or what was created) to the #product-docs Slack 
channel with a link.

Action review handling

For destructive or bulk operations, structure prompts to support a review step before execution. Ask the system to generate a summary report first, then act only after your explicit approval. This is especially important for operations that modify tickets, delete records, or send communications to external stakeholders. Build the review step directly into your prompt rather than relying on system-level safeguards alone.

Common pitfalls to avoid

Understanding common mistakes helps you avoid frustration across all Quick components.

General pitfalls

• Vague language that allows multiple interpretations.
• Overloading single prompts with too many requirements.
• Assuming context the AI doesn’t have.
• Neglecting to specify output format.
• Failing to test with realistic edge cases.
• Not documenting successful patterns for reuse.

Component-specific pitfalls

Quick Research: Writing search queries instead of research objectives. Omitting the audience context. Skipping the research plan review.

Quick Flows: Overloading a single prompt with too many operations. Skipping user input definitions. Ignoring error conditions and failure handling.

Quick Sight: Using vague metrics without specifying aggregation. Missing time context. Unclear dimensions for grouping. Ambiguous comparisons without a baseline.

Chat agents: Vague identity without defined expertise domains. Linking spaces with outdated information. Not providing fallback instructions. Launching without testing in Preview mode.

Action integrations: Vague action requests missing required parameters. Unclear dependencies between actions. Not planning for failures or authentication issues. Skipping review steps for destructive operations.

Putting learnings into practice

Start applying these techniques immediately with a phased approach:

Week 1: Foundation

• Identify your three most common Quick use cases.
• Rewrite existing prompts using the CRISPE framework from Part 1.
• Document the before/after results.

Week 2: Component focus

• Choose one Quick component (Quick Research, Quick Flows, Quick Sight, or Quick chat agents).
• Apply the component-specific patterns from this post to real workflows.
• Create reusable prompt templates.

Week 3: Advanced techniques

• Implement metadata-driven retrieval in your knowledge bases.
• Build a custom agent using the ARCHITECT framework.
• Create a complex Quick Flow with conditional logic.

Week 4: Scale and share

• Document your most effective prompts in a shared library.
• Train team members on successful patterns.
• Establish feedback mechanisms for continuous improvement.

Conclusion

Each Quick component has its own strengths and quirks, but the underlying principle remains the same: the more precisely you communicate your intent, the better the output. Quick Research needs clear objectives with audience context. Quick Flows need numbered steps with explicit triggers and conditions. Quick Sight needs structured queries with defined metrics and dimensions. Agents need sharp identities with clear boundaries. Actions need complete parameters with proper sequencing.

The prompt patterns in this post and Part 1 are starting points, not rigid templates. As you use them, you’ll develop intuition for what each component responds to best. You’ll learn that Quick Research agents reward precise audience definitions, that Quick Flows need explicit error handling, that Quick Sight queries work best with named aggregation types, and that chat agents perform better with narrow knowledge scopes. Document what works, share it with your team, and iterate continuously. The organizations that get the most from AI aren’t the ones with the most sophisticated technology. They’re the ones that have learned to communicate with it effectively.

Next steps

Ready to continue building your prompt engineering practice? Here’s where to go:

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About the authors

Daiquan N’kere

Daiquan is a Technical Account Manager and AI/ML Business Applications Specialist at AWS, where he partners with enterprise customers to architect cloud solutions and accelerate their digital transformation journeys. When he’s out of the office, you’ll find him exploring new destinations around the globe, spending quality time with his family, or wandering through museums discovering art and history.

Praney Mahajan

Praney is a Senior Technical Account Manager at AWS who partners with key enterprise customers as their strategic advisor. He is passionate about bridging technical solutions with business outcomes. He enjoys going on long drives with his family and playing cricket in his free time.

Vishnu Elangovan

Vishnu is a Worldwide Agentic AI Solution Architect with over a decade of experience in Applied AI/ML and Deep Learning. He loves building and tinkering with scalable AI/ML solutions and considers himself a lifelong learner. Vishnu is a trusted thought leader in the AI/ML community, regularly speaking at leading AI conferences and sharing his expertise on Agentic AI at top-tier events.

Dalien Ahiekpor

Dalien is a Senior Account Executive at AWS supporting enterprise Travel and Hospitality customers. With four years of experience as a Technical Account Manager before moving into his current role, Dalien brings a unique blend of technical depth and business acumen to every customer engagement. He is passionate about helping customers leverage cloud technology to transform the guest experience.