Cloud

If a picture is worth a thousand words, a video is worth a million. 

For creators, generative video holds the promise of bringing any story or concept to life. However, the reality has often been a frustrating cycle of “prompt and pray” – typing a prompt and hoping for a usable result, with little to no control over character consistency, cinematic quality, or narrative coherence.

This guide is a framework for directing Veo 3.1, our latest model that marks a shift from simple generation to creative control. Veo 3.1 builds on Veo 3, with stronger prompt adherence and improved audiovisual quality when turning images into videos. 

What you’ll learn in this guide:

• Learn Veo 3.1’s full range of capabilities on Vertex AI.

• Implement a formula to direct scenes with consistent characters and styles.

• Direct video and sound using professional cinematic techniques.

• Execute complex ideas by combining Veo with Gemini 2.5 Flash Image (Nano Banana) in advanced workflows.

Veo 3.1 model capabilities

First, it’s essential to understand the model’s full range of capabilities. Veo 3.1 brings audio to existing capabilities to help you craft the perfect scene. These features are experimental and actively improving, and we’re excited to see what you create as we iterate based on your feedback.

Core generation features:

• High-fidelity video: Generate video at 720p or 1080p resolution.

• Aspect ratio: 16:9 or 9:16

• Variable clip length: Create clips of 4, 6, or 8 seconds.

• Rich audio & dialogue: Veo 3.1 excels at generating realistic, synchronized sound, from multi-person conversations to precisely timed sound effects, all guided by the prompt.

• Complex scene comprehension: The model has a deeper understanding of narrative structure and cinematic styles, enabling it to better depict character interactions and follow storytelling cues.

Advanced creative controls:

• Improved image-to-video: Animate a source image with greater prompt adherence and enhanced audio-visual quality.

• Consistent elements with “ingredients to video”: Provide reference images of a scene, character, object, or style to maintain a consistent aesthetic across multiple shots. This feature now includes audio generation.

• Seamless transitions with “first and last frame”: Generate a natural video transition between a provided start image and end image, complete with audio.

• Add/remove object: Introduce new objects or remove existing ones from a generated video. Veo preserves the scene’s original composition.

• Digital watermarking: All generated videos are marked with SynthID to indicate the content is AI-generated.

Note: Add/remove object currently utilizes the Veo 2 model and does not generate  audio. 

A formula for effective prompts

A structured prompt yields consistent, high-quality results. Consider this five-part formula for optimal control.

[Cinematography] + [Subject] + [Action] + [Context] + [Style & Ambiance]

• Cinematography: Define the camera work and shot composition.

• Subject: Identify the main character or focal point.

• Action: Describe what the subject is doing.

• Context: Detail the environment and background elements.

• Style & ambiance: Specify the overall aesthetic, mood, and lighting.

Example prompt: Medium shot, a tired corporate worker, rubbing his temples in exhaustion, in front of a bulky 1980s computer in a cluttered office late at night. The scene is lit by the harsh fluorescent overhead lights and the green glow of the monochrome monitor. Retro aesthetic, shot as if on 1980s color film, slightly grainy.

Essential prompting techniques

Mastering these core techniques will give you granular control over every aspect of your generation.

The language of cinematography

The [Cinematography] element of your prompt is the most powerful tool for conveying tone and emotion.

• Camera movement: Dolly shot, tracking shot, crane shot, aerial view, slow pan, POV shot.

Crane shot example

Prompt: Crane shot starting low on a lone hiker and ascending high above, revealing they are standing on the edge of a colossal, mist-filled canyon at sunrise, epic fantasy style, awe-inspiring, soft morning light.

• Composition: Wide shot, close-up, extreme close-up, low angle, two-shot.

• Lens & focus: Shallow depth of field, wide-angle lens, soft focus, macro lens, deep focus.

Shallow depth of field example

Prompt: Close-up with very shallow depth of field, a young woman’s face, looking out a bus window at the passing city lights with her reflection faintly visible on the glass, inside a bus at night during a rainstorm, melancholic mood with cool blue tones, moody, cinematic.

Directing the soundstage 

Veo 3.1 can generate a complete soundtrack based on your text instructions.

• Dialogue: Use quotation marks for specific speech (e.g., A woman says, “We have to leave now.”).

• Sound effects (SFX): Describe sounds with clarity (e.g., SFX: thunder cracks in the distance).

• Ambient noise: Define the background soundscape (e.g., Ambient noise: the quiet hum of a starship bridge).

Mastering negative prompts

To refine your output, describe what you wish to exclude. For example, specify “a desolate landscape with no buildings or roads” instead of “no man-made structures”.

Prompt enhancement with Gemini

If you need to add more detail, use Gemini to analyze and enrich a simple prompt with more descriptive and cinematic language. 

Advanced creative workflows

While a single, detailed prompt is powerful, a multi-step workflow offers unparalleled control by breaking down the creative process into manageable stages. The following workflows demonstrate how to combine Veo 3.1’s new capabilities with Gemini 2.5 Flash Image (Nano Banana) to execute complex visions.

Workflow 1: The dynamic transition with “first and last frame” 

This technique allows you to create a specific and controlled camera movement or transformation between two distinct points of view.

Step 1: Create the starting frame: Use Gemini 2.5 Flash Image to generate your initial shot. 

Gemini 2.5 Flash Image prompt:

“Medium shot of a female pop star singing passionately into a vintage microphone. She is on a dark stage, lit by a single, dramatic spotlight from the front. She has her eyes closed, capturing an emotional moment. Photorealistic, cinematic.”

Step 2: Create the ending frame: Generate a second, complementary image with Gemini 2.5 Flash Image, such as a different POV angle. 

Gemini 2.5 Flash Image prompt:

“POV shot from behind the singer on stage, looking out at a large, cheering crowd. The stage lights are bright, creating lens flare. You can see the back of the singer’s head and shoulders in the foreground. The audience is a sea of lights and silhouettes. Energetic atmosphere.”

Step 3: Animate with Veo. Input both images into Veo using the First and Last Frame feature. In your prompt, describe the transition and the audio you want. 

Veo 3.1 prompt: “The camera performs a smooth 180-degree arc shot, starting with the front-facing view of the singer and circling around her to seamlessly end on the POV shot from behind her on stage. The singer sings “when you look me in the eyes, I can see a million stars.”

Workflow 2: Building a dialogue scene with “ingredients to video” 

This workflow is ideal for creating a multi-shot scene with consistent characters engaged in conversation, leveraging Veo 3.1’s ability to craft a dialogue.

Step 1: Generate your “ingredients”: Create reference images using Gemini 2.5 Flash Image for your characters and the setting.

Step 2: Compose the scene: Use the Ingredients to Video feature with the relevant reference images. 

Prompt “Using the provided images for the detective, the woman, and the office setting, create a medium shot of the detective behind his desk. He looks up at the woman and says in a weary voice, “Of all the offices in this town, you had to walk into mine.”

Prompt: “Using the provided images for the detective, the woman, and the office setting, create a shot focusing on the woman. A slight, mysterious smile plays on her lips as she replies, “You were highly recommended.”

Workflow 3: Timestamp prompting

This workflow allows you to direct a complete, multi-shot sequence with precise cinematic pacing, all within a single generation. By assigning actions to timed segments, you can efficiently create a full scene with multiple distinct shots, saving time and ensuring visual consistency.

Prompt example:

[00:00-00:02] Medium shot from behind a young female explorer with a leather satchel and messy brown hair in a ponytail, as she pushes aside a large jungle vine to reveal a hidden path.

[00:02-00:04] Reverse shot of the explorer's freckled face, her expression filled with awe as she gazes upon ancient, moss-covered ruins in the background. SFX: The rustle of dense leaves, distant exotic bird calls.

[00:04-00:06] Tracking shot following the explorer as she steps into the clearing and runs her hand over the intricate carvings on a crumbling stone wall. Emotion: Wonder and reverence.

[00:06-00:08] Wide, high-angle crane shot, revealing the lone explorer standing small in the center of the vast, forgotten temple complex, half-swallowed by the jungle. SFX: A swelling, gentle orchestral score begins to play.

Start creating with Veo 3.1 in Vertex AI

You now have the framework to direct Veo with precision. The best way to master these techniques is to apply them for real-world use cases.

For developers and enterprise users, the improved Veo 3.1 model is available in preview on Vertex AI via the API. This allows you to experiment with these advanced prompting workflows and build powerful, controlled video generation capabilities directly into your own applications.

^{Thanks to Anish Nangia, Sabareesh Chinta, and Wafae Bakkali for their contributions to prompting guidance for customers.}

As developers build increasingly sophisticated AI applications, they often encounter scenarios where substantial amounts of contextual information — be it a lengthy document, a detailed set of system instructions, a code base — need to be repeatedly sent to the model. While this data provides models with much-needed context for their responses, it often escalates costs and latency due to re-processing of the repeated tokens.

Enter Vertex AI context caching, which Google Cloud first launched in 2024 to tackle this very challenge. Since then, we have continued to improve Gemini serving for improved latency and costs for our customers. Caching works by allowing customers  to save and reuse precomputed input tokens. Some benefits include:

• Significant cost reduction: Customers pay only 10% of standard input token cost for cached tokens for all supported Gemini 2.5 and above models. For implicit caching, this cost saving is automatically passed on to you when a cache hit occurs. With explicit caching, this discount is guaranteed, providing predictable savings.

• Latency: Caching reduces latency by looking up previously computed content instead of recomputing. 

Let’s dive deeper into context caching and how you can get started. 

What is Vertex AI context caching?

As the name suggests, Vertex AI context caching aims to cache tokens of repeated content, and we offer two types:

• Implicit caching: Automatic caching which is enabled by default that provides cost savings when cache hits occur. Without needing to make any changes to your API calls, Vertex AI’s serving infrastructure automatically caches tokens and utilizes the states (KV pairs) from previous requests to speed up subsequent turns and provide cost savings. This continues for ensuing prompts, with retention based on overall load and reuse frequency, with caches always deleted within 24 hours. 

• Explicit caching: Users get more control of caching behavior by explicitly declaring the content to cache and then can refer to the cached content in the prompts as needed. Explicit caching discount is guaranteed, providing predictable savings. 

To support prompts and use cases of various sizes, we’ve enabled caching from a minimum of 2,048 tokens to the size of the models context window, which in the case of Gemini 2.5 Pro is over 1 million tokens. Cached content can be any of the modalities (text, pdf, image, audio or video) supported by Gemini multimodal models. For example, you can cache a large amount of text, audio, or video. See list of supported models here. 

To make sure users get the benefit of caching wherever and however they use Gemini, both forms of caching support global and regional endpoints. Further, Implicit caching is integrated with Provisioned Throughput to ensure production grade traffic gets the benefits of caching. To add an additional layer of security and compliance, Explicit caches can be encrypted using Customer Managed Encryption Keys (CMEKs).

Ideal use cases for context caching:

• Large-scale document processing: Cache lengthy contracts, case files, or (academic/ regulatory / finance / R&D) documents to repeatedly query for specific clauses, precedents, or compliance checks.

• For example, a Financial analyst using Gemini might upload dozens of documents such as annual reports that they want to subsequently query/analyze/extract/summarize and more. Instead of reuploading these documents each time they have a new question or want to start a new analysis, Context caching stores this already processed information. Once the analyst is done with their work, they can either manually clear the explicit cache or the implicit cache will automatically clear.

• For example, a customer support chatbot may have very detailed system instructions on how to respond to user questions, what information can be referenced when helping a user, and more. Instead of recomputing this each time a new customer conversation is started, compute it once and allow chatbots to reference this content. This can lead to significantly faster response times for chatbots and reduced overall costs.

Cost implications for implicit and explicit caching

Implicit caching: Enabled by default for all Google Cloud projects, as cache hits occur when repeated content is sent we automatically pass on a discount. The tokens that write to cache are charged as standard input tokens (no additional charge to write to cache).

Explicit caching:

1. Cached token count: When you create a CachedContent object, you pay a one-time fee for the initial caching of those tokens which is the standard input token cost. Subsequently, each time you use this cached content in a generate_content request, you are billed for the cached tokens at a 90% discount compared to sending them as regular input tokens.

2. Storage duration (TTL): You are also billed for the duration that the cached content is stored, based on its Time-To-Live (TTL). This is an hourly rate per million tokens stored prorated down to the minute level.

Best practices and how to optimize cache hit rate:

• Check the limitations: First, check that you are within the caching limitations, such as min cache size and supported models.

• Granularity: Add the cached/repeated portion of your context at the beginning of the prompt. Avoid caching small, frequently changing pieces.

• Monitor usage and costs: Regularly check your Google Cloud billing reports to understand the impact of caching on your expenses. To see how many tokens are cached, see cachedContentTokenCount in the UsageMetadata.

• Frequency: Implicit caches are cleared in 24 hours or less, a smaller time window of repeated requests will keep the cache available. 

For explicit caching specifically: 

• TTL Management: Set the ttl (Time-To-Live) carefully. A longer TTL incurs more storage cost but reduces recreation overhead. Balance this based on how long your context remains relevant and how frequently it’s accessed.

Get started:

Context caching is a game-changer for optimizing the performance and cost-efficiency of your AI applications. By intelligently leveraging this feature, you can significantly reduce redundant token processing, achieve faster response times, and ultimately build more scalable and cost-effective generative AI solutions.

Implicit caching is enabled by default for all GCP projects, so you can get started today. 

To get started with explicit caching check out our documentation (here is sample code to create your first cache), and a Colab notebook with common examples and code.

For engineering teams, a critical DORA metric is “Lead Time for Changes,” which measures the time from a code commit to its deployment in production. The 2025 State of AI-Assisted Development Report underscores that manual code review and approval processes are a significant bottleneck, negatively impacting this metric. The report reveals that a combined 60.2% of organizations have a lead time for changes of over a day, with 31.9% taking between a day and a week, and 28.3% taking between a week and a month.  This delay is often because senior developers are consumed by manual code reviews, where they enforce coding standards and identify anti-patterns.  

The challenge for IT leaders is clear: how do you remove this review bottleneck, especially when your code is spread across complex environments like GitHub Enterprise Cloud or on-premises servers?

Today, we’re introducing a solution to that challenge. We are launching a public preview of Gemini Code Assist on GitHub for enterprise customers, providing you with AI powered code reviews to meet the needs of enterprises.

Works where you work – now with GitHub Enterprise Support

You can’t fix a bottleneck you can’t reach. Our Gemini Code Assist on GitHub app, which is targeted for individual developers and OSS maintainers, was limited to github.com repositories. This enterprise version is built to support the most common code management options enterprises use from Github – from GitHub Enterprise Cloud (GHEC) to privately-hosted GitHub Enterprise Server (on-premises).

Through a secure integration, this feature is the “key” that unlocks code-review automation for your entire enterprise, regardless of which GitHub source code management option you use.

Manage at scale with organization-level controls

A primary challenge for platform admins is deploying and administration of AI assisted code reviews across their repository footprint.  This release is designed to make that simple. It gives platform teams the power to define a “golden path” for code quality and automate its enforcement from a central location.

These new org-level settings provide a baseline for all repositories, while still allowing individual teams to set their own repo-level style guides and configurations for further customization.

• Central custom style guides:. You can now define and enforce a single, organization-wide style guide. The AI agent automates the style and syntax feedback ensuring your specific rules are checked before a human ever sees the PR. This frees your human reviewers to focus only on high-value architecture and logic, dramatically shortening the review cycle.

• Org-wide configurations: To help you manage notifications at scale, you can now set the comment severity level across your entire organization from a central admin page, with more org-level configurations coming soon.

Enterprise-grade trust and compliance

This release is built on a foundation of enterprise trust and designed with Google Cloud level security and data protections.  The enterprise version of the code review agent operates under the Google Cloud Terms of Service. As part of our product, your code used as part of prompts to Gemini models and model responses are stateless in Google Cloud services and not stored.  To help protect the privacy of your data, we conform to Google’s privacy commitment with generative AI technologies including that Google doesn’t use your data to train our models without permission. 

This enterprise release is part of our broader effort to bring AI assistance across your entire software development lifecycle.

• Recently released: In case you missed it, we also recently launched the Code Review Gemini CLI Extension, bringing powerful AI assistance and agentic capabilities directly to your terminal

• What’s next: We’re just getting started. We are already developing new agentic loop capabilities for the GitHub app to help automate issue resolution and bug fixing. And, we continue to expand our support to other Source Code Management products.

Get started with the public preview today

We’ve created simple paths for you to get started, you can onboard your organization by starting in the Google Cloud Console.  Review our documentation to learn more about the setup and using the Code Review capability. Also see this video to learn more about onboarding.     

To allow your teams to test and use the agent’s full capabilities, the public preview also includes a significantly higher pull request quota than the individual developer tier.

The evolving security landscape demands more than just speed. It requires an intelligent, automated defense. Google Security Operations is an AI-powered platform built to deliver a modern agentic security operations center (SOC), where generative AI is woven into the fabric of your operations.

We go beyond traditional SIEM and SOAR by using AI as a force multiplier for your team. Gemini automates data analysis, guides investigations with clear insights, and streamlines response actions which can significantly reduce analyst toil and accelerate the security lifecycle. The result is a highly-efficient SOC that empowers your team to proactively hunt threats and stay ahead of adversaries.

We’re excited to share that Gartner has recognized Google as a Leader in the 2025 Gartner® Magic Quadrant™ for Security Information and Event Management (SIEM). In our second year of participation, we’ve been positioned in the leaders quadrant, which can be attributed to our “Ability to Execute” and “Completeness of Vision.” We’re especially proud that we were positioned highest on the “Completeness of Vision” axis amongst all participants. 

Gartner also acknowledges our AI and workflow capabilities. They said, “Use of AI is a core competency for Google and its SecOps platform offers strong AI functionality throughout many of the common activities and functions associated with SIEM operations. Its well integrated automation capabilities add to this overall strength.”

The intelligence-driven, AI-powered platform for the future

Google Security Operations delivers an open, scalable platform infused with Google’s market-leading threat intelligence and AI automation to help SOC teams accelerate their ability to detect, defend against, and respond to threats. Using our platform, customers have seen up to 240% return on investment (ROI) over three years, and have reduced the risk and cost of a breach by as much as 70%.

Teams can use Google Security Operations to detect more threats with less effort through a rich and growing set of curated detections out of the box. These detections are developed and continuously maintained by our team of threat researchers. SOC teams can also use natural language through Gemini to search their data, create detections and response playbooks. 

To streamline the work of the SOC, Google Security Operations offers an intuitive experience for security analysts that includes threat-centered case management; interactive, context-rich alert graphing; and automatic stitching together of entities. This experience can help teams investigate and respond with speed and precision using SOAR capabilities. As a direct result of these efficiencies, our customers have seen up to 50% faster mean time to respond (MTTR) and 65% faster mean time to investigate (MTTI).3 

Over the last year, we have added significant capabilities that we believe have contributed to our position as a Leader. 

Powerful AI workflow augmentation. As a core Google competency, and part of what makes our security operations platform effective, our early investment in generative AI capabilities has helped increase productivity. Strong, tightly-integrated AI functionality through Gemini in Security Operations can boost the everyday activities and functions of security operations teams. 

From using natural language to search, generate detections, and create playbooks, to more efficient investigations, our Gemini investigative chat assistant can help SOC analysts gain context and details about cases — and crucial recommendations on how to respond. The platform’s ease-of-use and gen AI capabilities are particularly empowering for new team members, which customers have noted reduced their time to productivity by up to 70%, and shifted up to 35% of security operations work to junior analysts.3

Google Security Operations offers automation that can help improve SOC team workflows and their ability to hunt for threats become more efficient and effective. We’re also continuing to evolve Google Security Operations automation with AI agents and our vision for the agentic SOC. 

The agentic SOC promises a fundamental shift for teams, where intelligent agents work alongside human analysts to autonomously take on routine tasks, augment human decision-making, automate workflows, and empower security experts to focus on the complex investigations and strategic challenges that truly demand human-in-the-loop expertise.

Building for our customers

We feel this ranking reflects our commitment to an open platform that easily integrates into customers’ existing ecosystems through supporting third-party data ingestion, providing federated deployments, enabling multi-tenancy management, and using automation and Gemini to augment security workflows. 

Ultimately, our platform’s value is best measured by the confidence it delivers to our customers. As a CISO from an insurance company put it, “In simple terms, Google SecOps is a mass risk-reducer. Threats that would have impacted our business no longer do, because we have greater observability, better mean time to detect, and better mean time to respond.”3

We are grateful to our customers’ trust and for partnering with us on this journey. We are committed to working together closely, and to ensure that our accelerated innovation helps you stay ahead of the evolving threat landscape.

Download a complimentary copy of the 2025 Gartner® Magic Quadrant™ for SIEM report today