Category: Cloud

Where you decide to run your web app is often a late-binding choice. Once you’ve finished coding something you like and done some localhost testing, you seek out a reasonable place that gives you a public IP address. Developers have no shortage of runtime host options, including hyperscalers, rented VMs from cheap regional providers, or targeted services from the likes of Firebase, Cloudflare, Vercel, Netlify, Fly.io, and a dozen others. I’m an unapologetic fanboy of Google Cloud Run—host scale-to-zero apps, functions, and jobs that offer huge resource configurations, concurrent calls, GPUs, and durable volumes with a generous free tier and straightforward pricing—and we just took the wraps of a handful of new ways to take a pile of code and turn it into a cloud endpoint.

Vibe-code a web app in Google AI Studio and one-click deploy to Cloud Run

Google AI Studio is really a remarkable. Build text prompts against our leading models, generate media with Gemini models, and even build apps. All at no cost. We just turned on the ability to do simple text-to-app scenarios, and added a button that deploys your app to Cloud Run.

First, I went to the “Build” pane and added a text prompt for my new app. I wanted a motivational quote printed on top of an image of an AI generated dog.

In one shot, I got the complete app including the correct backend AI calls to Gemini models for creating the motivational quote and generating a dog pic. So cool.

Time to ship it. There’s rocket ship icon on the top right. Assuming you’ve connected Google AI Studio to a Google Cloud account, you’re able to pick a project and one-click deploy.

It takes just a few seconds, and you get back the URL and a deep link to the app in Google Cloud.

Clicking that link shows that this is a standard Cloud Run instance, with the Gemini key helpfully added as an environment variable (versus hard coded!).

And of course, viewing the associated link takes me to my app that gives me simple motivation and happy dogs.

That’s such a simple development loop!

Create an .NET app in tools like Cursor and deploy it using the Cloud Run MCP server

Let’s say you’re using one of the MANY agentic development tools that make it simpler to code with AI assistance. Lots of you like Cursor. It supports MCP as a way to reach into other systems via tools.

We just shipped a Cloud Run MCP server, so you can make tools like Cursor aware of Cloud Run and support straightforward deployments.

I started in Cursor and asked it to build a simple REST API and picked Gemini 2.5 Pro as my preferred model. Cursor does most (all?) of the coding work for you if you want it to.

It went through a few iterations to land on the right code. I tested it locally to ensure the app would run.

Cursor has native support for MCP. I added a .cursor directory to my project and dropped in a mcp.json file in there. Cursor picked up the MCP entry, validated it, and showed me the available tools.

I asked Cursor to deploy my C# app. It explored the local folder and files to ensure it had what it needed.

Cursor realized it had a tool that could help, and proposed the “deploy_local_folder” tool from the Cloud Run MCP server.

After providing some requested values (location, etc), Cursor successfully deployed my .NET app.

That was easy. And this Cloud Run MCP server will work with any of your tools that understand MCP.

Push an open model from Google AI Studio directly to Cloud Run

Want to deploy a model to Cloud Run? It’s the only serverless platform I know of that offers GPUs. You can use tools like Ollama to deploy any open model to Cloud Run, and I like that we made even easier for Gemma fans. To see this integration, you pick various Gemma 3 editions in Google AI Studio.

Once you’ve done that, you’ll see a new icon that triggers a deployment directly to Cloud Run. Within minutes, you have an elastic endpoint providing inference.

It’s not hard to deploy open models to Cloud Run. This option makes it that much easier.

Deploy an Python agent built with the Agent Development Kit to Cloud Run with one command

The Agent Development Kit is an open source framework and toolset that devs use to build robust AI agents. The Python version reached 1.0 yesterday, and we launched a new Java version too. Here, I started with a Python agent I built.

Built into ADK are a few deployment options. It’s just code, so you can run it anywhere. But we’ve added shortcuts to services like Google Cloud’s Vertex AI Agent Engine and Cloud Run. Just one command puts my agent onto Cloud Run!

We don’t yet have this CLI deployment option for the Java ADK. But it’s also simple to use the Google Cloud CLI command to deploy a Java app or agent to Cloud Run with one command too.

Services like Cloud Run are ideal for your agents and AI apps. These built-in integrations for ADK help you get these agents online quickly.

Use a Gradio instance in Cloud Run to experiment with prompts after one click from Vertex AI Studio

How do you collaborate or share prompts with teammates? Maybe you’re using something like Google Cloud Vertex AI to iterate on a prompt yourself. Here, I wrote system instructions and a prompt for helping me prioritize my work items.

Now, I can click “deploy an app” and get a Gradio instance for experimenting further with my app.

This has public access by default, so I’ve got to give the ok.

After a few moments, I have a running Cloud Run app! I’m shown this directly from Vertex AI and have a link to open the app.

That link brings me to this Gradio instance that I can share with teammates.

The scalable and accessible Cloud Run is ideal for spontaneous exploration of things like AI prompts. I like this integration!

Ship your backend Java code to Cloud Run directly from Firebase Studio

Our final example looks at Firebase Studio. Have you tried this yet? It’s a free to use, full-stack dev environment in the cloud for nearly any type of app. And it supports text-to-app scenarios if you don’t want to do much coding yourself. There are dozens of templates, including one for Java.

I spun up a Java dev environment to build a web service.

This IDE will look familiar. Bring in your favorite extensions, and we’ve also pre-loaded this with Gemini assistance, local testing tools, and more. See here that I used Gemini to add a new REST endpoint to my Java API.

Here on the left is an option to deploy to Cloud Run!

After authenticating to my cloud account and picking my cloud project, I could deploy. After a few moments, I had another running app in Cloud Run, and had a route to make continuous updates.

Wow. That’s a lot of ways to go from code to cloud. Cloud Run is terrific for frontend or backend components, functions or apps, open source or commercial products. Try one of these integrations and tell me what you think!

Software is never going to be the same. Why would we go back to laborious research efforts, wasting time writing boilerplate code, and accepting so many interruptions to our flow state? Hard pass. It might not happen for you tomorrow, next month, or next year, but AI will absolutely improve your developer workflow.

Your AI-powered workflow may make use of more than one LLMs. Go for it. But we’ve done a good job of putting Gemini into nearly every stage of the new way of working. Let’s look at what you can do RIGHT NOW to build with Gemini.

• Build knowledge, plans, and prototypes with Gemini
• Build apps and agents with Gemini
• Build AI and data systems with Gemini
• Build a better day-2 experience with Gemini
• Build supporting digital assets with Gemini

Build knowledge, plans, and prototypes with Gemini

Are you still starting your learning efforts with a Google search? Amateurs 🙂 I mean, keep doing those so that we earn ad dollars. But you’ve got so many new ways to augment a basic search.

Gemini Deep Research is pretty amazing. Part of Gemini Advanced, it takes your query, searches the web on your behalf, and gives you a summary in minutes. Here I asked for help understanding the landscape of PostgreSQL providers, and it recapped results found in 240+ relevant websites from vendors, Reddit, analyst, and more.

You’ve probably heard of NotebookLM. Built with Gemini 2.0, it takes all sorts of digital content and helps you make sense of it. Including those hyper-realistic podcasts (“Audio Overviews”).

Planning your work or starting to flesh out a prototype? For free, Google AI Studio lets you interact with the latest Gemini models. Generate text, audio, or images from prompts. Produce complex codebases based on reference images or text prompts. Share your desktop and get live assistance on whatever task you’re doing. It’s pretty rad.

Google Cloud customers can get knowledge from Gemini in a few ways. The chat for Gemini Cloud Assist gives me an ever-present agent that can help answer questions or help me explore options. Here, I asked for a summary of the options for running PostgreSQL in Google Cloud. It breaks the response down by fully-managed, self-managed, and options for migration.

Gemini for Google Cloud blends AI-assistance into many different services. One way to use this is to understand existing SQL scripts, workflows, APIs, and more.

Trying to plan out your next bit of work? Google AI Studio or Vertex AI Studio can assist here too. In either service, you can pass in your backlog of features and bugs, maybe an architecture diagram or two, and even some reference PDFs, and ask for help planning out the next sprint. Pretty good!

Build apps and agents with Gemini

We can use Google AI Studio or Vertex AI Studio to learn things and craft plans, but now let’s look at how you’d actually build apps with Gemini.

You can work with the raw Gemini API. There are SDK libraries for Python, Node, Go, Dart, Swift, and Android. If you’re working with Gemini 2.0 and beyond, there’s a new unified SDK that works with both the Developer API and Enterprise API (Vertex). It’s fairly easy to use. I wrote a Google Cloud Function that uses the unified Gemini API to generate dinner recipes for whatever ingredients you pass in.

package function

import (
	"context"
	"encoding/json"
	"fmt"
	"log"
	"net/http"
	"os"

	"github.com/GoogleCloudPlatform/functions-framework-go/functions"
	"google.golang.org/genai"
)

func init() {
	functions.HTTP("GenerateRecipe", generateRecipe)
}

func generateRecipe(w http.ResponseWriter, r *http.Request) {
	ctx := context.Background()
	ingredients := r.URL.Query().Get("ingredients")

	if ingredients == "" {
		http.Error(w, "Please provide ingredients in the query string, like this: ?ingredients=pork, cheese, tortilla", http.StatusBadRequest)
		return
	}

	projectID := os.Getenv("PROJECT_ID")
	if projectID == "" {
		projectID = "default" // Provide a default, but encourage configuration
	}

	location := os.Getenv("LOCATION")
	if location == "" {
		location = "us-central1" // Provide a default, but encourage configuration
	}

	client, err := genai.NewClient(ctx, &genai.ClientConfig{
		Project:  projectID,
		Location: location,
		Backend:  genai.BackendVertexAI,
	})
	//add error check for err
	if err != nil {
		log.Printf("error creating client: %v", err)
		http.Error(w, "Failed to create Gemini client", http.StatusInternalServerError)
		return
	}

	prompt := fmt.Sprintf("Given these ingredients: %s, generate a recipe.", ingredients)
	result, err := client.Models.GenerateContent(ctx, "gemini-2.0-flash-exp", genai.Text(prompt), nil)
	if err != nil {
		log.Printf("error generating content: %v", err)
		http.Error(w, "Failed to generate recipe", http.StatusServiceUnavailable)
		return
	}

	if len(result.Candidates) == 0 {
		http.Error(w, "No recipes found", http.StatusNotFound) // Or another appropriate status
		return
	}

	recipe := result.Candidates[0].Content.Parts[0].Text // Extract the generated recipe text

	response, err := json.Marshal(map[string]string{"recipe": recipe})
	if err != nil {
		log.Printf("error marshalling response: %v", err)
		http.Error(w, "Failed to format response", http.StatusInternalServerError)
		return
	}

	w.Header().Set("Content-Type", "application/json")
	w.Write(response)
}

There are a lot agent frameworks out there right now. A LOT. Many of them have good Gemini support. You can build agents with Gemini using LangChain, LangChain4J, LlamaIndex, Spring AI, Firebase Genkit, and the Vercel AI SDK.

What coding tools can I use with Gemini? GitHub Copilot now supports Gemini models. Folks who love Cursor can choose Gemini as their underlying model. Same goes for fans of Sourcegraph Cody. Gemini Code Assist from Google Cloud puts AI-assisted tools into Visual Studio Code and the JetBrains IDEs. Get the power of Gemini’s long context, personalization on your own codebase, and now the use of tools to pull data from Atlassian, GitHub, and more. Use Gemini Code Assist within your local IDE, or in hosted environments like Cloud Workstations or Cloud Shell Editor.

Project IDX is another Google-provided dev experience for building with Gemini. Use it for free, and build AI apps, with AI tools. It’s pretty great for frontend or backend apps.

Maybe you’re building apps and agents with Gemini through low-code or declarative tools? There’s the Vertex AI Agent Builder. This Google Cloud services makes it fairly simple to create search agents, conversational agents, recommendation agents, and more. No coding needed!

Another options for building with Gemini is the declarative Cloud Workflows service. I built a workflow that calls Gemini through Vertex AI and summarizes any provided document.

# Summarize a doc with Gemini
main:
  params: [args]
  steps:
  - init:
      assign:
        - doc_url: ${args.doc_url}
        - project_id: ${args.project_id}
        - location: ${args.location}
        - model: ${args.model_name}
        - desired_tone: ${args.desired_tone}
        - instructions: 
  - set_instructions:
      switch:
        - condition: ${desired_tone == ""}
          assign:
            - instructions: "Deliver a professional summary with simple language."
          next: call_gemini
        - condition: ${desired_tone == "terse"}
          assign:
            - instructions: "Deliver a short professional summary with the fewest words necessary."
          next: call_gemini
        - condition: ${desired_tone == "excited"}
          assign:
            - instructions: "Deliver a complete, enthusiastic summary of the document."
          next: call_gemini
  - call_gemini:
      call: googleapis.aiplatform.v1.projects.locations.endpoints.generateContent
      args:
        model: ${"projects/" + project_id + "/locations/" + location + "/publishers/google/models/" + model}
        region: ${location}
        body:
          contents:
            role: user
            parts:
              - text: "summarize this document"
              - fileData: 
                  fileUri: ${doc_url}
                  mimeType: "application/pdf"
          systemInstruction: 
            role: user
            parts:
              - text: ${instructions}
          generation_config:  # optional
            temperature: 0.2
            maxOutputTokens: 2000
            topK: 10
            topP: 0.9
      result: gemini_response
  - returnStep:
      return: ${gemini_response.candidates[0].content.parts[0].text}

Similarly, its sophisticated big-brother, Application Integration, can also interact with Gemini through drag-and-drop integration workflows. These sorts of workflow tools help you bake Gemini predictions into all sorts of existing processes.

After you build apps and agents, you need a place to host them! In Google Cloud, you’ve could run in a virtual machine (GCE), Kubernetes cluster (GKE), or serverless runtime (Cloud Run). There’s also the powerful Firebase App Hosting for these AI apps.

There are also two other services to consider. For RAG apps, we now offer the Vertex AI RAG Engine. I like this because you get a fully managed experience for ingesting docs, storing in a vector database, and performing retrieval. Doing LangChain? LangChain on Vertex AI offers a handy managed environment for running agents and calling tools.

Build AI and data systems with Gemini

In addition to building straight-up agents or apps, you might build backend data or AI systems with Gemini.

If you’re doing streaming analytics or real-time ETL with Dataflow, you can build ML pipelines, generate embeddings, and even invoke Gemini endpoints for inference. Maybe you’re doing data analytics with frameworks like Apache Spark, Hadoop, or Apache Flink. Dataproc is a great service that you can use within Vertex AI, or to run all sorts of data workflows. I’m fairly sure you know what Colab is, as millions of folks per month use it for building notebooks. Colab and Colab Enterprise offer two great ways to build data solutions with Gemini.

Let’s talk about building with Gemini inside your database. From Google Cloud SQL, Cloud Spanner, and AlloyDB, you can create “remote models” that let you interact with Gemini from within your SQL queries. Very cool and useful. BigQuery also makes it possible to work directly with Gemini from my SQL query. Let me show you.

I made a dataset from the public “release notes” dataset from Google Cloud. Then I made a reference to the Gemini 2.0 Flash model, and then asked Gemini for a summary of all a product’s release notes from the past month.

-- create the remote model
CREATE OR REPLACE MODEL
`[project].public_dataset.gemini_2_flash`
REMOTE WITH CONNECTION `projects/[project]/locations/us/connections/gemini-connection`
OPTIONS (ENDPOINT = 'gemini-2.0-flash-exp');

-- query an aggregation of responses to get a monthly product summary
SELECT * 
FROM
 ML.GENERATE_TEXT(
    MODEL `[project].public_dataset.gemini_2_flash`,
    (
      SELECT CONCAT('Summarize this month of product announcements by rolling up the key info', monthly_summary) AS prompt
      FROM (
        SELECT STRING_AGG(description, '; ') AS monthly_summary
        FROM `bigquery-public-data`.`google_cloud_release_notes`.`release_notes` 
        WHERE product_name = 'AlloyDB' AND DATE(published_at) BETWEEN '2024-12-01' AND '2024-12-31'
      )
    ),
    STRUCT(
      .05 AS TEMPERATURE,
      TRUE AS flatten_json_output)
    )

How wild is that? Love it.

You can also build with Gemini in Looker. Build reports, visualizations, and use natural language to explore data. See here for more.

And of course, Vertex AI helps you build with Gemini. Build prompts, fine-tune models, manage experiments, make batch predictions, and lots more. If you’re working with AI models like Gemini, you should give Vertex AI a look.

Build a better day-2 experience with Gemini

It’s not just about building software with Gemini. The AI-driven product workflow extends to post-release activities.

Have to set up least-privilege permissions for service accounts? Build the right permission profile with Gemini.

Something goes wrong. You need to get back to good. You can build faster resolution plans with Gemini. Google Cloud Logging supports log summarization with Gemini.

Ideally, you know when something goes wrong before your customers notice. Synthetic monitors are one way to solve that. We made it easy to build synthetic monitors with Gemini using natural language.

You don’t want to face security issues on day-2, but it happens. Gemini is part of Security Command Center where you can build search queries and summarize cases.

Gemini can also help you build billing reports. I like this experience where I can use natural language to get answers about my spend in Cloud Billing.

Build supporting digital assets with Gemini

The developer workflow isn’t just about code artifacts. Sometimes you create supporting assets for design docs, production runbooks, team presentations, and more.

Use the Gemini app (or our other AI surfaces) to generate images. I do this all the time now!

Building slides? Writing docs? Creating spreadsheets? Gemini for Workspace gives you some help here. I use this on occasion to refine text, generate slides or images, and update tables.

Maybe you’re getting bored with static image representations and want some more videos in your life? Veo 2 is frankly remarkable and might be a new tool for your presentation toolbox. Consider a case where you’re building a mobile app that helps people share cars. Maybe produce a quick video to embed in the design pitch.

AI disrupts the traditional product development workflow. Good! Gemini is part of each stage of the new workflow, and it’s only going to get better. Consider introducing one or many of these experiences to your own way of working in 2025.

The ability to use your own codebase to customize the suggestions from an AI coding assist is a big deal. This feature—available in products like Gemini Code Assist, GitHub Copilot, and Tabnine—gives developers coding standards, data objects, error messages, and method signatures that they recognize from previous projects. Data shows that the acceptance rate for AI coding assistants goes way up when devs get back trusted results that look familiar. But I don’t just want up-to-date and familiar code that *I* wrote. How can I make sure my AI coding assistant gives me the freshest and best code possible? I used code customization in Gemini Code Assist to reference Google Cloud’s official code sample repos and now I get AI suggestions that feature the latest Cloud service updates and best practices for my preferred programming languages. Let me show you how I did it.

Last month, I showed how to use local codebase awareness in Gemini Code Assist (along with its 128,000 input token window) to “train” the model on the fly using code samples or docs that an LLM hasn’t been trained on yet. It’s a cool pattern, but also requires upfront understanding of what problem you want to solve, and work to stash examples into your code repo. Can I skip both steps?

Yes, Gemini Code Assist Enterprise is now available and I can point to existing code repos in GitHub or GitLab. When I reference a code repo, Google Cloud automatically crawls it, chunks it up, and stores it (encrypted) in a vector database within a dedicated project in my Google Cloud environment. Then, the Gemini Code Assist plugin uses that data as part of a RAG pattern when I ask for coding suggestions. By pointing at Google Cloud’s code sample repos—any best practice repo would apply here—I supercharge my recommendations with data the base LLM doesn’t have (or prioritize).

Step #0 – Prerequisites and considerations

Code customization is an “enterprise” feature of Gemini Code Assist, so it requires a subscription to that tier of service. There’s a promotional $19-per-month price until March of 2025, so tell your boss to get moving.

Also, this is currently available in US, European, and Asian regions, you may need to request geature access via a form (depending on when you read this), and today it works with GitHub.com and GitLab.com repos, although on-premises indexing is forthcoming. Good? Good. Let’s keep going.

Step #1 – Create the source repo

One wrinkle here is that you need to own the repos you ask Gemini Code Assist to index. You can’t just point at any random repo to index. Deal breaker? Nope.

I can just fork an existing repo into my own account! For example, here’s the Go samples repo from Google Cloud, and the Java one. Each one is stuffed with hundreds of coding examples for interacting with most of Google Cloud’s services. These repos are updated multiple times per week to ensure they include support for all the latest Cloud service features.

I went ahead and forked each repo in GitHub. You can do it via the CLI or in the web console.

I didn’t overthink it and kept the repository name the same.

Gemini Code Assist can index up to 950 repos (and more if really needed), so you could liberally refer to best-practice repos that will help your developers write better code.

Any time I want to refresh my fork to grab the latest code sample updates, I can do so.

Step #2 – Add a reference to the source repo

Now I needed to reference these repos for later code customization. Google Cloud Developer Connect is a service that maintains connections to source code sitting outside Google Cloud.

I started by choosing GitHub.com as my source code environment.

Then I named my Developer Connect connection.

Then I installed a GitHub app into my GitHub account. This app is what enables the loading of source data into the customization service. From here, I chose the specific repos that I wanted available to Developer Connect.

When finished, I had one of my own repos, and two best practice repos all added to Developer Connect.

That’s it! Now to point these linked repos to Gemini Code Assist.

Step #3 – Add a Gemini Code Assist customization index

I had just two CLI commands to execute.

First, I created a code customization index. You’ve got one index per Cloud project (although you can request more) and you create it with one command.

Next, I created a repository group for the index. You use these to control access to repos, and could have different ones for different dev audiences. Here’s where you actually point to a given repo that has the Developer Connect app installed.

I ran this command a few times to ensure that each of my three repos was added to the repository group (and index).

Indexing can take up to 24 hours, so here’s where you wait. After a day, I saw that all my target repos were successfully indexed.

Whenever I sync the fork with the latest updates to code samples, Gemini Code Assist will index the updated code automatically. And my IDE with Gemini Code Assist will have the freshest suggestions from our samples repo!

Step #4 – Use updated coding suggestions

Let’s prove that this worked.

I looked for a recent commit to the Go samples repos that the base Gemini Code Assist LLM wouldn’t know about yet. Here’s one that has new topic-creation parameters for our Managed Kafka service. I gave the prompt below to Gemini Code Assist. First, I used a project and account that was NOT tied to the code customization index.

//function to create a topic in Google Cloud Managed Kafka and include parameters for setting replicationfactor and partitioncount

The coding suggestion was good, but incomplete as it was missing the extra configs the service can now accept.

When I went to my Code Assist environment that did have code customization turned on, you see that the same prompt gave me a result that mirrored the latest Go sample code.

I tried a handful of Java and Go prompts, and I regularly (admittedly, not always) got back exactly what I wanted. Good prompt engineering might have helped me reach 100%, but I still appreciated the big increase in quality results. It was amazing to have hundreds of up-to-date Google-tested code samples to enrich my AI-provided suggestions!

AI coding assistants that offer code customization from your own repos are a difference maker. But don’t stop at your own code. Index other great code repos that represent the coding standards and fresh content your developers need!

Trust. Without trust, AI coding assistants won’t become a default tool in a developer’s toolbox. Trust is the #1 concern of devs today, and it’s something I’ve struggled with in regards to getting the most relevant answers from an LLM. Specifically, am I getting back the latest information? Probably not, given that LLMs have a training cutoff date. Your AI coding assistant probably doesn’t (yet) know about Python 3.13, the most recent features of your favorite cloud service, or the newest architectural idea shared at a conference last week. What can you do about that?

To me, this challenge comes up in at least three circumstances. There are entirely new concepts or tools that the LLM training wouldn’t know about. Think something like pipe syntax as an alternative to SQL syntax. I wouldn’t expect a model trained last year to know about that. How about updated features to existing libraries or frameworks? I want suggestions that reflect the full feature set of the current technology and I don’t want to accidentally do something the hard (old) way. An example? Consider the new “enum type” structured output I can get from LangChain4J. I’d want to use that now! And finally, I think about improved or replicated framework libraries. If I’m upgrading from Java 8 to Java 23, or Deno 1 to Deno 2, I want to ensure I’m not using deprecated features. My AI tools probably don’t know about any of these.

I see four options for trusting the freshness of responses from your AI assistant. The final technique was brand new to me, and I think it’s excellent.

1. Fine-tune your model
2. Use retrieval augmented generation (RAG)
3. Ground the results with trusted sources
4. “Train” on the fly with input context

Let’s briefly look at the first three, and see some detailed examples of the fourth.

Fine-tune your model

Whether using commercial or open models, they all represent a point-in-time based on their training period. You could choose to repeatedly train your preferred model with fresh info about the programming languages, frameworks, services, and patterns you care about.

The upside? You can get a model with knowledge about whatever you need to trust it. The downside? It’s a lot of work—you’d need to craft a healthy number of examples and must regularly tune the model. That could be expensive, and the result wouldn’t naturally plug into most AI coding assistance tools. You’d have to jump out of your preferred coding tool to ask questions of a model elsewhere.

Use RAG

Instead of tuning a serving a custom model, you could choose to augment the input with pre-processed content. You’ll get back better, more contextual results when taking into account data that reflects the ideal state.

The upside? You’ll find this pattern increasingly supported in commercial AI assistants. This keeps you in your flow without having to jump out to another interface. GitHub Copilot offers this, and now our Gemini Code Assist provides code customization based on repos in GitHub or GitLab. With Code Assist, we handle the creation and management of the code index of your repos, and you don’t have to manually chunk and store your code. The downside? This only works well if you’ve got the most up-to-date data in an indexed source repo. If you’ve got old code or patterns in there, that won’t help your freshness problem. And while these solutions are good for extra code context, they may not support a wider range of possible context sources (e.g. text files).

Ground the results

This approach gives you more confidence that the results are accurate. For example, Google Cloud’s Vertex AI offers “ground with Google Search” so that responses are matched to real, live Google Search results.

If I ask a question about upgrading an old bit of Deno code, you can see that the results are now annotated with reference points. This gives me confidence to some extent, but doesn’t necessarily guarantee that I’m getting the freshest answers. Also, this is outside of my preferred tool, so it again takes me out of a flow state.

Train on the fly

Here’s the approach I just learned about from my boss’s boss, Keith Ballinger. I complained about freshness of results from AI assistance tools, and he said “why don’t you just train it on the fly?” Specifically, pass the latest and greatest reference data into a request within the AI assistance tool. Mind … blown.

How might it handle entirely new concepts or tools? Let’s use that pipe syntax example. In my code, I want to use this fresh syntax instead of classic SQL. But there’s no way my Gemini Code Assist environment knows about that (yet). Sure enough, I just get back a regular SQL statement.

But now, Gemini Code Assist supports local codebase awareness, up to 128,000 input tokens! I grabbed the docs for pipe query syntax, saved as a PDF, and then asked Google AI Studio to produce a Markdown file of the docs. Note that Gemini Code Assist isn’t (yet) multi-modal, so I need Markdown instead of passing in a PDF or image. I then put a copy of that Markdown file in a “training” folder within my app project. I used the new @ mention feature in our Gemini Code Assist chat to specifically reference the syntax file when asking my question again.

Wow! So by giving Gemini Code Assist a reference file of pipe syntax, it was able to give me an accurate, contextual, and fresh answer.

What about updated features to existing libraries or frameworks? I mentioned the new feature of LangChain4J for the Gemini model. There’s no way I’d expect my coding assistant to know about a feature added a few days ago. Once again, I grabbed some resources. This time, I snagged the Markdown doc for Google Vertex AI Gemini from the LangChain4J repo, and converted a blog post from Guillaume to Markdown using Google AI Studio.

My prompt to the Gemini Code Assist model was “Update the service function with a call to Gemini 1.5 Flash using LangChain4J. It takes in a question about a sport, and the response is mapped to an enum with values for baseball, football, cricket, or other.” As expected, the first response was a good attempt, but it wasn’t fully accurate. And it used a manual way to map the response to an enum.

What if I pass in both of those training files with my prompt? I get back exactly the syntax I wanted for my Cloud Run Function!

So great. This approach requires me to know what tech I’m interested in up front, but still, what an improvement!

Final example. How about improved or replicated framework libraries? Let’s say I’ve got a very old Deno app that I created when I first got excited about this excellent JavaScript runtime.

// from https://deno.com/blog/v1.35#denoserve-is-now-stable
async function handleHttp(conn: Deno.Conn) {
  // `await` is needed here to wait for the server to handle the request
  await (async () => {
    for await (const r of Deno.serveHttp(conn)) {
      r.respondWith(new Response("Hello World from Richard"));
    }
  })();
}

for await (const conn of Deno.listen({ port: 8000 })) {
  handleHttp(conn);
}

This code uses some libraries and practices that are now out of date. When I modernize this app, I want to trust that I’m doing it the best way. Nothing to fear! I grabbed the Deno 1.x to 2.x migration guide, a blog post about the new approach to web servers, and the launch blog for Deno 2. The result? Impressive, including a good description of why it generated the code this way.

I could imagine putting the latest reference apps into a repo and using Gemini Code Assist’s code customization feature to pull that automatically into my app. But this demonstrated technique gives me more trust in the output of tool when freshness is paramount. What do you think?