Generating content with AI models

Genkit provides a unified interface for working with generative AI models from any supported provider. Configure a model plugin once, then call any model through the same API—making it easy to combine multiple models or swap one out as your app evolves.

If you want to run the code examples on this page, first complete the steps in the Get started guide. All of the examples assume that you have already installed Genkit as a dependency in your project.

Before you can use Genkit to start generating content, you need to load and configure a model plugin. If you’re coming from the Get started guide, you’ve already done this. Otherwise, see the Get started guide or the individual plugin’s documentation and follow the steps there before continuing.

In Genkit, the primary interface through which you interact with generative AI models is the genkit.Generate() function.

The simplest genkit.Generate() call specifies the model you want to use and a text prompt:

package main

import (
    "context"
    "log"

    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    "github.com/firebase/genkit/go/plugins/googlegenai"
)

func main() {
    ctx := context.Background()

    g := genkit.Init(ctx,
        genkit.WithPlugins(&googlegenai.GoogleAI{}),
        genkit.WithDefaultModel("googleai/gemini-flash-latest"),
    )

    resp, err := genkit.Generate(ctx, g,
        ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
    )
    if err != nil {
        log.Fatalf("could not generate model response: %v", err)
    }

    log.Println(resp.Text())
}

When you run this brief example, it will print out some debugging information followed by the output of the genkit.Generate() call, which will usually be Markdown text as in the following example:

## The Blackheart's Bounty

**A hearty stew of slow-cooked beef, spiced with rum and molasses, served in a
hollowed-out cannonball with a side of crusty bread and a dollop of tangy
pineapple salsa.**

**Description:** This dish is a tribute to the hearty meals enjoyed by pirates
on the high seas. The beef is tender and flavorful, infused with the warm spices
of rum and molasses. The pineapple salsa adds a touch of sweetness and acidity,
balancing the richness of the stew. The cannonball serving vessel adds a fun and
thematic touch, making this dish a perfect choice for any pirate-themed
adventure.

Run the script again and you’ll get a different output.

The preceding code sample sent the generation request to the default model, which you specified when you configured the Genkit instance.

You can also specify a model for a single genkit.Generate() call:

resp, err := genkit.Generate(ctx, g,
    ai.WithModelName("googleai/gemini-pro-latest"),
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
)

A model string identifier looks like providerid/modelid, where the provider ID (in this case, googleai) identifies the plugin, and the model ID is a plugin-specific string identifier for a specific version of a model.

The Google AI and Vertex AI plugins register no models when Genkit starts. Every model ID is resolved the first time you name it, so any model the provider serves works, whether or not the plugin knows about it. The plugin’s curated list decides what the Developer UI offers and what capabilities Genkit assumes for an ID it recognizes. It is a starting point, not a limit.

These examples also illustrate an important point: when you use genkit.Generate() to make generative AI model calls, changing the model you want to use is a matter of passing a different value to the model parameter. By using genkit.Generate() instead of the native model SDKs, you give yourself the flexibility to more easily use several different models in your app and change models in the future.

So far you have only seen examples of the simplest genkit.Generate() calls. However, genkit.Generate() also provides an interface for more advanced interactions with generative models, which you will see in the sections that follow.

Some models support providing a system prompt, which gives the model instructions as to how you want it to respond to messages from the user. You can use the system prompt to specify characteristics such as a persona you want the model to adopt, the tone of its responses, and the format of its responses.

If the model you’re using supports system prompts, you can provide one with the ai.WithSystem() option:

resp, err := genkit.Generate(ctx, g,
    ai.WithSystem("You are a food industry marketing consultant."),
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
)

For models that don’t support system prompts, ai.WithSystem() simulates it by modifying the request to appear like a system prompt.

The genkit.Generate() function takes a ai.WithConfig() option, through which you can specify optional settings that control how the model generates content:

resp, err := genkit.Generate(ctx, g,
    ai.WithModelName("googleai/gemini-flash-latest"),
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
    ai.WithConfig(&genai.GenerateContentConfig{
        MaxOutputTokens: 500,
        StopSequences:   []string{"<end>", "<fin>"},
        Temperature:     genai.Ptr[float32](0.5),
        TopP:            genai.Ptr[float32](0.4),
        TopK:            genai.Ptr[float32](50),
    }),
)

The exact parameters that are supported depend on the individual model and model API. However, the parameters in the previous example are common to almost every model. The following is an explanation of these parameters:

MaxOutputTokens

LLMs operate on units called tokens. A token usually, but does not necessarily, map to a specific sequence of characters. When you pass a prompt to a model, one of the first steps it takes is to tokenize your prompt string into a sequence of tokens. Then, the LLM generates a sequence of tokens from the tokenized input. Finally, the sequence of tokens gets converted back into text, which is your output.

The maximum output tokens parameter sets a limit on how many tokens to generate using the LLM. Every model potentially uses a different tokenizer, but a good rule of thumb is to consider a single English word to be made of 2 to 4 tokens.

As stated earlier, some tokens might not map to character sequences. One such example is that there is often a token that indicates the end of the sequence: when an LLM generates this token, it stops generating more. Therefore, it’s possible and often the case that an LLM generates fewer tokens than the maximum because it generated the “stop” token.

StopSequences

You can use this parameter to set the tokens or token sequences that, when generated, indicate the end of LLM output. The correct values to use here generally depend on how the model was trained, and are usually set by the model plugin. However, if you have prompted the model to generate another stop sequence, you might specify it here.

Note that you are specifying character sequences, and not tokens per se. In most cases, you will specify a character sequence that the model’s tokenizer maps to a single token.

The temperature, top-p, and top-k parameters together control how “creative” you want the model to be. This section provides very brief explanations of what these parameters mean, but the more important point is this: these parameters are used to adjust the character of an LLM’s output. The optimal values for them depend on your goals and preferences, and are likely to be found only through experimentation.

Temperature

LLMs are fundamentally token-predicting machines. For a given sequence of tokens (such as the prompt) an LLM predicts, for each token in its vocabulary, the likelihood that the token comes next in the sequence. The temperature is a scaling factor by which these predictions are divided before being normalized to a probability between 0 and 1.

Low temperature values—between 0.0 and 1.0—amplify the difference in likelihoods between tokens, with the result that the model will be even less likely to produce a token it already evaluated to be unlikely. This is often perceived as output that is less creative. Although 0.0 is technically not a valid value, many models treat it as indicating that the model should behave deterministically, and to only consider the single most likely token.

High temperature values—those greater than 1.0—compress the differences in likelihoods between tokens, with the result that the model becomes more likely to produce tokens it had previously evaluated to be unlikely. This is often perceived as output that is more creative. Some model APIs impose a maximum temperature, often 2.0.

TopP

Top-p is a value between 0.0 and 1.0 that controls the number of possible tokens you want the model to consider, by specifying the cumulative probability of the tokens. For example, a value of 1.0 means to consider every possible token (but still take into account the probability of each token). A value of 0.4 means to only consider the most likely tokens, whose probabilities add up to 0.4, and to exclude the remaining tokens from consideration.

TopK

Top-k is an integer value that also controls the number of possible tokens you want the model to consider, but this time by explicitly specifying the maximum number of tokens. Specifying a value of 1 means that the model should behave deterministically.

You can experiment with the effect of these parameters on the output generated by different model and prompt combinations by using the Developer UI. Start the developer UI with the genkit start command and it will automatically load all of the models defined by the plugins configured in your project. You can quickly try different prompts and configuration values without having to repeatedly make these changes in code.

Given that each provider or even a specific model may have its own configuration schema or warrant certain settings, it may be error prone to set separate options using ai.WithModelName() and ai.WithConfig() since the latter is not strongly typed to the former.

To pair a model with its config, you can create a model reference that you can pass into the generate call instead:

model := googlegenai.ModelRef("googleai/gemini-flash-latest", &genai.GenerateContentConfig{
    MaxOutputTokens: 500,
    StopSequences:   []string{"<end>", "<fin>"},
    Temperature:     genai.Ptr[float32](0.5),
    TopP:            genai.Ptr[float32](0.4),
    TopK:            genai.Ptr[float32](50),
})

resp, err := genkit.Generate(ctx, g,
    ai.WithModel(model),
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
)
if err != nil {
    log.Fatal(err)
}

The constructor for the model reference will enforce that the correct config type is provided which may reduce mismatches.

When using generative AI as a component in your application, you often want output in a format other than plain text. Even if you’re just generating content to display to the user, you can benefit from structured output simply for the purpose of presenting it more attractively to the user. But for more advanced applications of generative AI, such as programmatic use of the model’s output, or feeding the output of one model into another, structured output is a must.

In Genkit, you can request structured output from a model by specifying an output type when you call genkit.Generate():

type MenuItem struct {
    Name        string   `json:"name"`
    Description string   `json:"description"`
    Calories    int      `json:"calories"`
    Allergens   []string `json:"allergens"`
}

resp, err := genkit.Generate(ctx, g,
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
    ai.WithOutputType(MenuItem{}),
)
if err != nil {
    log.Fatal(err) // One possible error is that the response does not conform to the type.
}

Model output types are specified as JSON schema using the invopop/jsonschema package. This provides runtime type checking, which bridges the gap between static Go types and the unpredictable output of generative AI models. This system lets you write code that can rely on the fact that a successful generate call will always return output that conforms to your Go types.

When you specify an output type in genkit.Generate(), Genkit does several things behind the scenes:

• Augments the prompt with additional guidance about the selected output format. This also has the side effect of specifying to the model what content exactly you want to generate (for example, not only suggest a menu item but also generate a description, a list of allergens, and so on).
• Verifies that the output conforms to the schema.
• Marshals the model output into a Go type.

To get structured output from a successful generate call, call Output() on the model response with an empty value of the type:

var item MenuItem
if err := resp.Output(&item); err != nil {
    log.Fatal(err)
}

log.Printf("%s (%d calories, %d allergens): %s\n",
    item.Name, item.Calories, len(item.Allergens), item.Description)

Alternatively, you can use genkit.GenerateData() for a more succinct call:

item, resp, err := genkit.GenerateData[MenuItem](ctx, g,
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
)
if err != nil {
    log.Fatal(err)
}
if item == nil {
    // The response carried no text to parse. resp says why.
    log.Fatalf("no menu item: finish reason %q, %d interrupts, %d tool requests",
        resp.FinishReason, len(resp.Interrupts()), len(resp.ToolRequests()))
}

log.Printf("%s (%d calories, %d allergens): %s\n",
    item.Name, item.Calories, len(item.Allergens), item.Description)

This function requires the output type parameter but automatically sets the ai.WithOutputType() option and calls ModelResponse.Output() before returning the value.

Check the three results in order: the error, then the value, then the fields. genkit.GenerateData() returns a nil value with a live response and no error whenever the response carried no text to parse, which is what a turn holding a tool request, an interrupt, or media looks like. That is a legitimate answer, not a failure, so it is yours to interpret: read resp.Interrupts(), resp.ToolRequests(), and resp.FinishReason. A response with no content at all and an ordinary finish reason is an error instead, and resp is nil there, so never read it before checking err.

The basic-structured sample carries the whole pattern, including the streaming form.

For schemas that are shared across your application (such as those used in .prompt files), you can register them with genkit.DefineSchemasFor() and reference them by name. Each value registers a schema under its Go type’s name, so one call covers as many types as your app has:

// Register the schemas once at startup
genkit.DefineSchemasFor(g, MenuItem{}, MenuRequest{})

// Reference by name in generate calls
resp, err := genkit.Generate(ctx, g,
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
    ai.WithOutputSchemaName("MenuItem"),
)

genkit.DefineSchemaFor[T](g) is the single-type form of the same thing, and genkit.DefineSchema(g, name, schema) registers a schema you wrote by hand under a name of your choosing. All three live in package genkit.

This is particularly useful when working with Dotprompt, as you can define your types once in Go and reference them in .prompt files by name, avoiding duplicate schema definitions.

Note in the prior example that the genkit.Generate() call can result in an error. One possible error can happen when the model fails to generate output that conforms to the schema. The best strategy for dealing with such errors will depend on your exact use case, but here are some general hints:

• Try a different model. For structured output to succeed, the model must be capable of generating output in JSON. The most powerful LLMs like Gemini are versatile enough to do this; however, smaller models, such as some of the local models you would use with Ollama, might not be able to generate structured output reliably unless they have been specifically trained to do so.

• Simplify the schema. LLMs may have trouble generating complex or deeply nested types. Try using clear names, fewer fields, or a flattened structure if you are not able to reliably generate structured data.

• Retry the genkit.Generate() call. If the model you’ve chosen only rarely fails to generate conformant output, you can treat the error as you would treat a network error, and retry the request using some kind of incremental back-off strategy.

When the model stops for a reason other than finishing its answer, holding it to the schema would report the wrong problem: you would see a parse failure where the real news is that a safety filter fired or a tool paused the turn. So Genkit skips the parsing step that rewrites the message whenever the finish reason is blocked, aborted, interrupted, or the catch-all other. The finish reason survives to you instead. unknown is deliberately not in that set, because plugins map any provider reason they do not recognize to it.

What that means at the call site:

• genkit.GenerateData() gives you a nil value, the response, and no error when such a response carried no text. Read resp.FinishReason and resp.FinishMessage for a block, and resp.Interrupts() for a pause.
• genkit.GenerateDataStream() ends with a final value whose Output is the zero value of your type, again with no error, so check val.Response there rather than the output.
• A response that stopped early but still carries conforming text parses as usual, so the skip costs you nothing in the common case.

The output format decides two things: how the model is asked to write its answer, and how that answer is parsed back into Go values. Genkit registers exactly five, and any of them can be selected explicitly:

jsonl and array need an array schema, so the output type has to be a slice. ai.WithOutputEnums() sets the schema and the format together, so it is the whole of what an enum output needs. Selecting a name that is not registered fails with INVALID_ARGUMENT before the model is ever called.

A format does not replace your schema: the schema still comes from the output type, and only the way the model is asked to write it out changes.

// One item per line instead of one JSON array.
for val, err := range genkit.GenerateDataStream[[]MenuItem](ctx, g,
    ai.WithOutputFormat(ai.OutputFormatJSONL),
    ai.WithPrompt("Invent four menu items for a pirate themed restaurant."),
) {
    if err != nil {
        log.Fatal(err)
    }
    if val.Done {
        log.Printf("%d items\n", len(val.Output))
        break
    }
    for _, item := range val.Chunk {
        log.Println(item.Name)
    }
}

The basic-formats sample puts json, jsonl, and enum side by side over one story premise, a flow for each.

The format decides what one streamed chunk contains, which is the part that catches people out. Parsing a chunk with chunk.Output() or reading val.Chunk from genkit.GenerateDataStream() gives you:

• text: everything accumulated so far. Use chunk.Text() instead if you want only the text that just arrived.
• json: the whole value so far, filling in field by field. Each chunk supersedes the one before it, so replace what you are holding rather than appending to it. Partial string values are normal mid-stream.
• jsonl: the items that finished since the last chunk, plus the item still being written. That trailing item arrives again, further along, on the next chunk, so a consumer that wants only finished items has to spot the repeat.
• array: only the items that became complete since the last chunk, and never a half-written one. items = append(items, val.Chunk...) is correct, and an empty first chunk is normal.
• enum: the empty string until the whole value has arrived. There is effectively nothing to stream, so do not put a progress indicator on it.

The final response differs too. json, jsonl, and enum validate it against the schema, so a missing required field or a label outside the set is an error. array does not, so a missing field reaches you as a zero Go field rather than as a failure.

Register your own format with genkit.DefineFormats(), then select it by name. The name comes from the formatter’s own Name() method:

// csvFormatter asks the model for comma-separated values.
type csvFormatter struct{}

func (csvFormatter) Name() string { return "csv" }

func (csvFormatter) Handler(schema map[string]any) (ai.FormatHandler, error) {
    return &csvHandler{}, nil
}

type csvHandler struct {
    text   string // text accumulated for the turn being parsed
    index  int    // the index of that turn
    cursor int    // how much of text has already been handed over
}

func (h *csvHandler) Instructions() string {
    return "Output ONLY comma-separated values on a single line. No prose, no code fences."
}

func (h *csvHandler) Config() ai.ModelOutputConfig {
    return ai.ModelOutputConfig{Format: "csv", ContentType: "text/csv"}
}

// ParseMessage is a passthrough: parsing belongs in ParseOutput.
func (h *csvHandler) ParseMessage(m *ai.Message) (*ai.Message, error) { return m, nil }

// ParseOutput parses the final message: every field, in order.
func (h *csvHandler) ParseOutput(m *ai.Message) (any, error) {
    return strings.Split(m.Text(), ","), nil
}

// ParseChunk returns only the fields completed since the previous chunk. The
// handler is reused across turns, so it resets when chunk.Index changes.
func (h *csvHandler) ParseChunk(chunk *ai.ModelResponseChunk) (any, error) {
    if chunk.Index != h.index {
        h.text, h.index, h.cursor = "", chunk.Index, 0
    }
    for _, p := range chunk.Content {
        if p.IsText() {
            h.text += p.Text
        }
    }
    done := strings.LastIndex(h.text, ",") // a field is complete once its comma arrives
    if done < h.cursor {
        return []string{}, nil
    }
    fresh := strings.Split(h.text[h.cursor:done], ",")
    h.cursor = done + 1
    return fresh, nil
}

genkit.DefineFormats(g, csvFormatter{})

resp, err := genkit.Generate(ctx, g,
    ai.WithPrompt("List three colors."),
    ai.WithOutputFormat("csv"),
)
if err != nil {
    log.Fatal(err)
}

var colors []string
if err := resp.Output(&colors); err != nil {
    log.Fatal(err)
}

Instructions(), Config(), and ParseMessage() make up ai.FormatHandler, the minimum. ParseOutput() and ParseChunk() add ai.StreamingFormatHandler, and without them both resp.Output() and chunk.Output() fail. A name can be claimed only once: registering one that is already taken panics, and that includes the five built-ins, so they cannot be replaced.

When generating large amounts of text, you can improve the experience for your users by presenting the output as it’s generated—streaming the output. A familiar example of streaming in action can be seen in most LLM chat apps: users can read the model’s response to their message as it’s being generated, which improves the perceived responsiveness of the application and enhances the illusion of chatting with an intelligent counterpart.

There are two shapes, and the question that picks between them is whether your code has anything to do with the chunks. If you are only passing them on to your own caller, hand your callback to ai.WithStreaming() and let the chunks travel untouched. If you have to look at them, range over genkit.GenerateStream().

Use genkit.GenerateStream() when the caller has to act on chunks as they arrive. It returns an iterator you can range over:

stream := genkit.GenerateStream(ctx, g,
    ai.WithPrompt("Suggest a complete menu for a pirate themed restaurant."),
)

for result, err := range stream {
    if err != nil {
        log.Fatal(err)
    }
    if result.Done {
        // Final response is available
        log.Println("Complete response:", result.Response.Text())
        break
    }
    // Just the text that arrived with this chunk
    log.Println(result.Chunk.Text())
}

The iterator yields *ai.ModelStreamValue values, where:

• result.Chunk contains the streamed chunk data
• result.Done indicates whether this is the final result
• result.Response contains the complete response (only available when Done is true)

For streaming structured output with strong typing, use genkit.GenerateDataStream[T]():

type MenuItem struct {
    Name        string `json:"name"`
    Description string `json:"description"`
}

stream := genkit.GenerateDataStream[MenuItem](ctx, g,
    ai.WithPrompt("Invent a menu item for a pirate themed restaurant."),
)

for result, err := range stream {
    if err != nil {
        log.Fatal(err)
    }
    if result.Done {
        // result.Output is strongly typed as MenuItem
        log.Printf("Final: %s - %s\n", result.Output.Name, result.Output.Description)
        break
    }
    // result.Chunk is also strongly typed as MenuItem, holding everything
    // parsed so far
    if result.Chunk.Name != "" {
        log.Printf("Got name: %s\n", result.Chunk.Name)
    }
}

With GenerateDataStream[T], both the streamed chunks and the final output are strongly typed, making your code safer and more predictable.

Ask for the value type, GenerateDataStream[MenuItem], rather than the pointer type. The two behave differently on chunks that parse to nothing, which is what a code fence or a line of prose ahead of the JSON looks like: those chunks are dropped only when the type parameter can be nil, so [*MenuItem] filters them while [MenuItem] delivers a zero-value struct. Reading a half-filled value the same way as one whose fields have not arrived yet is the simpler contract, and it is what basic-structured uses. Guard on a field you care about, as above, rather than assuming every chunk carries something new.

When your code is only handing the chunks onward, pass the callback to ai.WithStreaming() and let genkit.Generate() return the finished response as usual. Inside a streaming flow this is the whole job, because the flow’s own sendChunk is already the callback the option wants:

genkit.DefineStreamingFlow(g, "menuFlow",
    func(ctx context.Context, topic string, sendChunk ai.ModelStreamCallback) (string, error) {
        resp, err := genkit.Generate(ctx, g,
            ai.WithPrompt("Suggest a complete menu for a %s themed restaurant.", topic),
            ai.WithStreaming(sendChunk),
        )
        if err != nil {
            return "", err
        }
        return resp.Text(), nil
    },
)

The basic sample puts that flow next to a non-streaming one, so the pair shows what streaming does and does not change.

The callback is an ordinary function, so use it anywhere you want to process chunks inline or feed callback-based code you already have:

resp, err := genkit.Generate(ctx, g,
    ai.WithPrompt("Suggest a complete menu for a pirate themed restaurant."),
    ai.WithStreaming(func(ctx context.Context, chunk *ai.ModelResponseChunk) error {
        // Process each chunk as it arrives
        log.Println(chunk.Text())
        return nil
    }),
)
if err != nil {
    log.Fatal(err)
}

log.Println(resp.Text())

The examples you’ve seen so far have used text strings as model prompts. While this remains the most common way to prompt generative AI models, many models can also accept other media as prompts. Media prompts are most often used in conjunction with text prompts that instruct the model to perform some operation on the media, such as to caption an image or transcribe an audio recording.

The ability to accept media input and the types of media you can use are completely dependent on the model and its API. For example, the Gemini 2.5 series of models can accept images, video, and audio as prompts.

To provide a media prompt to a model that supports it, use ai.WithPromptParts() instead of ai.WithPrompt(). It fills the same user prompt slot but takes parts rather than text, so a picture and a question travel together as one turn. This example specifies an image using a publicly accessible HTTPS URL.

resp, err := genkit.Generate(ctx, g,
    ai.WithModelName("googleai/gemini-flash-latest"),
    ai.WithPromptParts(
        ai.NewTextPart("Compose a poem about this image."),
        ai.NewMediaPart("image/jpeg", "https://example.com/photo.jpg"),
    ),
)

You can also pass media data directly by encoding it as a data URL. For example:

image, err := os.ReadFile("photo.jpg")
if err != nil {
    log.Fatal(err)
}

resp, err := genkit.Generate(ctx, g,
    ai.WithModelName("googleai/gemini-flash-latest"),
    ai.WithPromptParts(
        ai.NewTextPart("Compose a poem about this image."),
        ai.NewMediaPart("image/jpeg", "data:image/jpeg;base64,"+base64.StdEncoding.EncodeToString(image)),
    ),
)

All models that support media input support both data URLs and HTTPS URLs. Some model plugins add support for other media sources. For example, the Vertex AI plugin also lets you use Cloud Storage (gs://) URLs.

ai.WithMessages() is still how you supply the turns leading up to the prompt, and those messages can carry media parts of their own. The two options fill different slots, so a request can use both:

resp, err := genkit.Generate(ctx, g,
    ai.WithModelName("googleai/gemini-flash-latest"),
    ai.WithMessages(history...),
    ai.WithPromptParts(
        ai.NewTextPart("Compose a poem about this image."),
        ai.NewMediaPart("image/jpeg", "https://example.com/photo.jpg"),
    ),
)

The basic-media sample covers both ways to attach a picture, and goes on to editing, generating, and animating one.

• As an app developer, the primary way you influence the output of generative AI models is through prompting. Read Managing prompts with Dotprompt to learn how Genkit helps you develop effective prompts and manage them in your codebase.
• Although genkit.Generate() is the nucleus of every generative AI powered application, real-world applications usually require additional work before and after invoking a generative AI model. To reflect this, Genkit introduces the concept of flows, which are defined like functions but add additional features such as observability and simplified deployment. To learn more, see Defining AI workflows.

There are techniques your app can use to reap even more benefit from LLMs.

• One way to enhance the capabilities of LLMs is to prompt them with a list of ways they can request more information from you, or request you to perform some action. This is known as tool calling or function calling. Models that are trained to support this capability can respond to a prompt with a specially-formatted response, which indicates to the calling application that it should perform some action and send the result back to the LLM along with the original prompt. Genkit has library functions that automate both the prompt generation and the call-response loop elements of a tool calling implementation. See Tool calling to learn more.
• Retrieval-augmented generation (RAG) is a technique used to introduce domain-specific information into a model’s output. This is accomplished by inserting relevant information into a prompt before passing it on to the language model. A complete RAG implementation requires you to bring several technologies together: text embedding generation models, vector databases, and large language models. See Retrieval-augmented generation (RAG) to learn how Genkit simplifies the process of coordinating these various elements.