AI coach: Best practices

To optimize AI coach suggestions, you must provide the following information:

A set of written instructions
A conversation transcript
Data from the API

In return, AI coach provides applicable instructions and suggested agent responses.

Recommended uses

To receive the most helpful suggestions, use AI coach in the following scenarios.

Up selling and cross selling

When a contact center agent must offer another product or service, AI coach can provide the following helpful information.

Suggest when to start sales and when not to attempt sales at all.
Ask probing questions to understand customer needs.
Recommend a product from a short list based on the conversation context.
Suggest how to handle objections.

Customer retention

When a contact center agent tries to convince someone to remain with their company, AI coach can provide the following helpful information.

Suggest when to make retention offers.
Ask probing questions to understand the customer's cancellation reason.
Recommend retention offers based on the conversation context.

One step automation with tools

AI coach can also automate following repetitive business processes based on a customer service conversation.

Retrieve order status when the customer is authenticated and provided the order number.
Cancel an order when the order number and cancellation reason are available.
Configure risky automations to require manual review and approval.

Risky operations

Risky operations are automated business processes, which can cause heavy financial loss, a data breach, or regulatory non-compliance. Some examples include writing databases and retrieving sensitive information without authentication. AI coach can suggest timing and extract parameters from a conversation, but risky operations require manual review and approval and shouldn't be fully automated.

AI coach instructions

Write a set of instructions to teach an AI coach what actions an agent should take and when those actions are needed throughout a customer service conversation. The instructions can specify any agent action, such as ask the customer for their account PIN, check the order status and inform the customer, or submit a cancellation request.

AI coach instructions include the following information:

Display title: Helps you manage multiple instructions. The display title is not visible to the LLM.
Display details: Specifies the static content that the model should return when the instruction is applicable. Display details can include a URL, shortcut to an internal tool or system, cheatsheet for the agents, or backend API call. These details are only accessible to the human agents. The display detail is not visible to the model.
Condition: Specifies when in the conversation an instruction is applicable. The condition is visible to the model.
Agent action: Specifies what steps the agent should take when an instruction is applicable. The agent action is visible to the model.
System action: Specifies what tools to use for automation when an instruction is applicable. The system action is visible to the model.

The condition, agent action, and system action affect AI coach's suggestions.

Condition

The condition must be clear, concise, and self-contained. You can use a brief description of a customer's issue. Write the condition as if you're describing it to an average person who isn't familiar with your product. Following are a few examples of useful conditions:

The customer wants to cancel an order.
The customer wants to purchase a new phone.
The customer is having a very slow internet connection.

Example

The following is an example of a condition and an agent action that you might use.

Condition: Customer asks to update the shipping address to a new address.
Agent action: If the new address wasn't provided yet, ask the customer which is the new address that they want to use for the shipment.

In general, only show the text of a company policy after the relevant condition is met. In this example, that means AI coach only displays the cancellation policy after the customer expresses that they want to cancel. In the following example input, the intended topic of the conversation is reflected in both the display title and the condition. The content of the text shown to the agent is defined in the display detail.

The input to AI coach:

display_title: order cancellation
display_detail: Company cancellation policy: Orders cannot be canceled more than 30 days after they have been placed. Only the cancellation team can process order cancellations.
condition: the customer wants to cancel an order
agent_action: N/A

Agent action

Ideally, you can verify each step of the agent action based on the following three sources of information:

Instructions: See details in the previous section.
Conversation transcript: Documents what an agent and a customer say throughout a customer service conversation.
Ingested data: Consists of relevant customer data that is not found directly in the conversation, such as recent transactions, bills, and ongoing promotions.

An example of some ingested data that AI coach could use:

ingested_data_key: Product Recommendation
ingested_data_value:
* Microwave-safe, glass food container set, $29.99
* Gila Complete Window Film Application Kit, $19.99

Missing data

If an agent action requires information that's missing from the other pieces of the instruction and the conversation transcript, then the API that provides the ingested data must also provide the missing information.

Avoid writing any action that requires information not found in those sources.
If you cannot find the corresponding data, avoid:
- Explaining the bill to the customer.
- Informing the customer of their balance, due date, or order status.

Multiple verbs

While the preceding list of agent actions contains only one verb, indicating an action for the agent to take, a step can have more than one verb. For example, if a business wants their agents to recommend a specific product and relate that product back to the condition all in one response, then you can write a single agent action step as in the following input.

display_title: recommend device protection
display_detail:
 trade-in process: [list steps for trade-in]
 device protection plan: [list plan details]
condition: the customer has issues/questions about trade-in
agent_action:
Recommend the device protection plan and tell the customer it can help them get better trade-in values in the future.

Sample response output

An agent action can also utilize ingested data and specify a template for the sample response output. For example, if a business has multiple products to recommend and wants their agents to make recommendations that are relevant to the conversation context, then the agent action can prompt AI coach to make specific suggestions in the sample response outputs. Plus, this example illustrates how to write an agent action that prompts AI coach to generate sample responses which follow a template.

display_title: cross-selling
display_detail: see `Product Recommendations` for a relevant product
condition: the customer is inquiring about product availability
agent_action:
Summarize to the customer what product you have been discussing, then recommend a relevant product from the section titled "Product Recommendation" and provide a brief explanation of how the two products are relevant to each other. Use this template for sample responses: "Since we've been talking about ..., I'd like to recommend ... because ...".

Some examples:
- For TVs, recommend TV stands.
- For storage, recommend label writers.
- For slow cookers and microwaves, recommend food container sets.
- For air conditioners, recommend humidifiers.

Multi-step action

Lastly, the agent action can consist of more than one step. A multi-step agent action indicates how the agent should proceed through a conversation when a single condition is met.

Consider the following example for an order cancellation conversation the agent needs to ask a series of questions before they can process the cancellation. Each step will generate a separate sample response.

display_title: order cancellation
display_detail:
 cancellation policy: [list policies]
 look up `[order number]` in ordering system
 how to cancel an order [list steps]
condition: the customer wants to cancel an order
agent_action:
1. Ask the customer for the order number.
2. Ask the customer why they want to cancel the order.
3. Inform the customer you are working on the cancellation.
4. Inform the customer that you have canceled the order.
5. Inform the customer that they will receive the refund in 5-7 business days.

Note that a step like cancel the order can be difficult to verify without additional integrations. In the previous example, we replace it with the step inform the customer you are working on the cancellation.

System action

In system action, we can reference tools that are already defined. This section in an instruction specifies which tools to consider when the instruction condition is met. The reference to a tool should be in this format: ${tool:tool_name|action_name}. For details on how to set up tools, see the OpenAPI and Integration Connectors tools page.

The following are some system action examples:

Example 1: List the tool and action names.

...
system_action:
${tool:order_management|cancel_order}, ${tool:order_management|check_order}

Example 2: Add conditional behavior.

...
system_action:
* if "RESPONSE TO ${TOOL:ORDER_MANAGEMENT|CANCEL_ORDER}" is already present without any error, skip ${tool:order_management|cancel_order} 
* if the order number has been provided, use ${tool:order_management|cancel_order} 
* if "RESPONSE TO ${TOOL:GETORDERDETAILS|GETORDERDETAILS}" shows errors or failures, retry ${tool: order_management | cancel_order}

Usage of overarching guidance in AI coach

AI coach adds a configurable prompt section called overarching guidance, which specifies the following:

Glossary
Guidelines of style, format, and tone for the sample response suggestions
Important notes on how to use and follow the instructions

Glossary

The following glossary clearly defines vulnerable customer and account authentication. With this glossary in the overarching guidance, you can use terms in the instructions without the need to repetitively define them like in earlier versions of AI coach.

#### Vulnerable customer

A customer is considered a vulnerable customer when at least one of the following is true:

- The customer is experiencing financial hardship.
- The customer has a physical disability or chronic health condition.
- The customer is facing domestic abuse or is in an unsafe situation.

#### Account authentication

Account authentication is considered completed if the account PIN provided by the customer matches the account PIN in the data section *Account PIN*.

Style, format, and tone guidelines

Follow these style, format, and tone guidelines in your sample responses:

Always be empathetic, patient, and reassuring.
Avoid overly casual language or slang. Maintain a respectful yet warm tone.
Responses must always acknowledge the customer's feelings or concerns explicitly (example: "I understand you're frustrated").
Use short sentences, and avoid complex language or jargon.
Never blame or contradict the customer. Instead, gently guide them toward correct information.
Begin every response with a clear acknowledgment of the customer's primary concern.

Instruction-following notes

Add notes on how to follow your instructions. To improve automatic suggestions, you can also use notes to specify the thinking or reasoning process.

Examples

Example 1: Configure the model behavior

### Follow instructions
* An instruction is applicable if and only if both of the following are true:
* The condition is met by the conversation and the contexts.
* Some of the agent actions are not completed yet. (If all the agent actions are done, then the instruction no longer applies.)
* Notes on following the applicable instructions:
* Do not suggest any sample responses or agent actions that are not specified in the applicable instructions.
* Do not ask for information not specified in the instructions.
* If an agent action step is to ask for information that the customer has already provided, then the step should be skipped.
* **Your suggested responses should fit into the conversation flow**. For example, if the customer and the support agent has moved on to a new issue, you should stop making suggestions for the old issue.
* Each sample response (or agent action suggestion) must be concise and only carry out one step in the applicable instruction. Do not cover multiple steps within one sample response (or agent action suggestion).
* Avoid repetition:
* Do not suggest sample responses or agent actions that the agent has already said or done earlier in the conversation.
* Do not ask for information that is already available in the conversation or the contexts.
* For product names, when you provide parameters in system action suggestions, provide the full names, including available specs.
* If multiple instructions lead to the same or similar suggestion, give one suggestion, but with multiple sources.

Example 2: Adjust the instruction priorities and specify the thinking process to get more accurate suggestions

### Notes on how to use or follow instructions
If there are multiple instructions applicable at the same time, **suggest only one instruction as applicable based on the following priority (from highest to lowest)**:
* [Highest priority] security related

Example: Account authentication is required before anything else.

* [High priority] customer retention
* [Medium priority] issue resolution
* [Low priority] upselling or cross-selling

### Thinking process
Before you make any suggestions, answer the following questions in this output section:
* For each instruction, is it applicable? If yes, what is its priority?
* Which instruction has the highest priority?

The following example illustrates the same instruction priority with examples of what the model should suggest under certain circumstances.

### Notes on how to use or follow instructions
If there are multiple instructions applicable at the same time, **suggest only one instruction as applicable based on the following priority (from highest to lowest)**:
* [Highest priority] security related

Examnple: Account authentication is required before anything else.

* [High priority] customer retention
* [Medium priority] issue resolution
* [Low priority] up-selling or cross-selling

#### Examples to follow
<example_1>
<instructions>
...
instruction I1
condition: the customer wants to inquire about their current plan
agent action: inform the customer of what plan they have and what features they get
...
instruction I2
condition: the authentication is not done yet
agent action: ask for the account PIN
...
</instructions>
<conversation>
customer: can you tell me what plan I have?
</conversation>
<good_suggestion>
may I have your account PIN, please?
</good_suggestion>
<bad_suggestion>
sure, let me pull up your account information.
</bad_suggestion>
<comment>
Both instructions I1 and I2 are applicable, but I1 should have the highest priority because it's security related. Therefore, asking for the account PIN is a good suggestion.
</comment>
</example_1>

AI coach output language

AI coach generates suggestions in the specified output language. The model decides the output language of the suggestions if output language is not specified.

AI coach outputs

AI coach returns either or both of the following results:

Applicable instructions: Multiple applicable instructions can be returned and each has its display title and details.
Sample responses: AI coach also generates samples of how an agent might respond in a conversation. These sample responses are based on the instructions, the conversation contexts, and the ingested data. The sample response can also be empty.

Use cases

AI coach has three main use cases:

Intent detection: This is when you only want to show the agents which instructions are applicable at the moment and you don't need the sample responses. You can rely on the ingested data (URLs, webhooks, cheatsheets, etc.) stored in the display detail to make suggestions to the agents or to trigger ingested API calls.
Step-by-step guidance: This is when you want to show the agents step-by-step guidance through sample responses.
Product recommendation: This is when you want to use AI coach to guide the agents on when to recommend products and what products to recommend to their customers. The product recommendation candidates can be generated by the user's recommendation system or a static catalog.

Quality improvement

The following recommendations for writing AI coach instructions can help improve the quality of sample responses, but don't prevent AI coach from making unexpected suggestions.

Text formatting for display details

Use Markdown syntax to display details in a rich format (links, bold text, etc.). This is rendered in the Google Cloud console simulator, but also helps to implement in the web interface.

Text formatting for agent action

When the AI coach often misses a certain step or requirement, use bold text to help guide the model to pay more attention to a specific piece of text. To create bold text with Markdown syntax, add two asterisks on either side of the text.

display_title: order cancellation
display_detail:
 cancellation policy: [list policies]
 look up `[order number]` in ordering system
 how to cancel an order [list steps]
condition: the customer wants to cancel an order
agent_action:
1. ask the customer for the order number
2. **ask the customer why they want to cancel the order**
3. inform the customer you are working on the cancellation
4. inform the customer that you have canceled the order
5. inform the customer that they will receive the refund in 5-7 business days

Bold text becomes less effective if, for example, every step of the agent action is made bold. So use this technique sparingly.

Adding concrete examples

When AI coach fails to give the sample responses you expect, adding some examples of what you want to see to the agent action can help. The following detailed example illustrates how lightweight examples (highlighted) can be used:

display_title: smartwatch recommendation
display_detail: see `Smartwatch Catalog` for a relevant smartwatch
condition: the customer recently purchased phones
agent action:
recommend a specific smartwatch from the section titled "Smartwatch Catalog"; the recommendation should match the recently purchased phones from the section titled "Recent Purchases".
Following are some examples:
- For Galaxy phones, recommend Galaxy watch.
- For iPhones, recommend Apple Watch.
- For Pixel phones, recommend Pixel Watch. 
- For other phones, recommend Fitbit Sense.

Without the lightweight examples, AI coach would suggest Apple Watch for Androids or list all the available smartwatches. With the lightweight examples, the suggestions are precise.

Message templates

For control over the wording and format of the generated sample responses, append a message template or even a static fixed message to the agent action. Unlike the lightweight examples, these templates indicate the exact wording that should be used in a sample response. Following is an example with the message templates highlighted.

display_title: bill explanation
display_detail:
 look up `[account number]`
 check `Recent Bills` to compare amounts
 https://www.[company name].com/promos
condition: the customer wonders why the bill is getting higher this month
agent_action:
1. ask the customer for the account number. use this fixed message for sample responses: "May I have your account number, please?"
2. compare the bills in the last two months from the section titled "Recent Bills" and inform the customer of the extra charge. use this template for sample responses: "Compared with last month, this month you have an extra charge for ..., which costs ..."

Guided reasoning

If one or more instructions specify a goal without enough steps or without clarifying hidden reasoning to reach the goal, modify the steps for clarity. For example, when an agent is told to recommend a product, the suggested product should be relevant to the conversation. Help AI coach make relevant suggestions by adding a subtask to the start of the agent action that refers to the conversation context.

display_title: cross-selling
display_detail: sample response
condition: the customer is inquiring about product availability
agent_action:
summarize to the customer what product you have been discussing, then recommend a relevant product from the section titled "Product Recommendation" and provide a brief explanation of how the two products are relevant to each other. Use this template for sample responses: "Since we've been talking about ..., I'd like to recommend ... because ...".

In the example, the main task is to suggest a product relevant to the conversation. AI coach can miss the point and recommend the first product listed in the Product Recommendation section. Adding the subtask can dramatically improve relevance. Also add a message template at the end of the agent action so the subtask result fits smoothly into the sample responses.

Add suggestion deduplication

The AI coach suggestion deduplication feature prevents agents from being shown duplicate or highly similar suggestions. Use the SuggestionDedupingConfig field in the generator proto to compare the suggestion components (instructions, sample responses, etc.) against a history of recent suggestions. You can tune the sensitivity of similarity using a configurable threshold. The DuplicateCheckResult message provides the outcomes of the deduplication check within the suggestion response.

Configure suggestion deduplication

To manage duplicate suggestions from AI coach, configure the SuggestionDedupingConfig within your generator's configuration.

Configuration fields

SuggestionDedupingConfig has the following fields:

enable_deduping: Set this boolean field to true to enable the suggestion deduplication feature. Setting it to false or leaving it unset disables deduplication. Example:
```
suggestion_deduping_config {
enable_deduping: true
}
```
similarity_threshold: Use this float-type threshold for similarity-based deduplication on certain fields. The deduplication process considers suggestions with a similarity score higher than this value as duplicates. The score ranges from 0.0 to 1.0. Setting this field to 0.0 or leaving it unset uses a default threshold of 0.8. Example:
```
suggestion_deduping_config {
enable_deduping: true
similarity_threshold: 0.85
}
```

How deduplication works

When enable_deduping is true, you can compare suggestions against historical suggestions. The deduplication process assesses duplication for each of the following suggestion fields:

Instructions (applicable_instructions): Checks for exact matches with the history suggestions.
Sample responses (sample_responses): Checks for similarity between current suggestion and the history suggestions. The deduplication flags duplicate results if the similarity score exceeds the similarity threshold.
Agent actions (agent_action_suggestions): Checks for similarity between current suggestion and the history suggestions, similar to sample responses. The deduplication flags duplicate results if the similarity score exceeds the similarity threshold. The results of the deduplication checks are returned in the DuplicateCheckResult message.

Output: DuplicateCheckResult

The AgentCoachingSuggestion message contains fields for different types of suggestions (e.g., AgentActionSuggestion, SampleResponse). Each of these fields will include a DuplicateCheckResult message to provide information about potential duplicates found in the history. Example:

// Example within AgentActionSuggestion
message AgentActionSuggestion {
  // ... other fields ...
  DuplicateCheckResult duplicate_check_result = 3;
}

// Duplication check for the suggestion.
message DuplicateCheckResult {
  // The duplicate suggestions.
  repeated DuplicateSuggestion duplicate_suggestions = 1 [
    (google.api.field_behavior) = OUTPUT_ONLY,
    (datapol.semantic_type) = ST_NOT_REQUIRED
  ];

  // The duplicate suggestion details.
  message DuplicateSuggestion {
    // The answer record ID of the past duplicate suggestion.
    string answer_record = 1 [
      (google.api.field_behavior) = OUTPUT_ONLY,
      (datapol.semantic_type) = ST_IDENTIFYING_ID
    ];
    // Sources for the suggestion.
    Sources sources = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
    // The index of the duplicate suggestion in the past suggestion list.
    int32 suggestion_index = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
    // The similarity score of between the past and current suggestion.
    // This is only populated for fields using similarity checks.
    float similarity_score = 4 [(google.api.field_behavior) = OUTPUT_ONLY];
  }
}

Example configuration

The example generator configuration enables the suggestion deduplication feature by setting enable_deduping to true. Additionally, it sets the similarity_threshold value to 0.75. This means that if a new suggestion's text (for sample responses and agent actions) has a similarity score greater than or equal to 0.75 with any of the historical suggestions, the deduplication flags it as a duplicate with DuplicateCheckResult.

# Example Generator configuration with Deduping enabled
generator {
  # ... other generator fields ...

  suggestion_deduping_config {
    enable_deduping: true
    similarity_threshold: 0.75 # Optional: Adjusts sensitivity for similarity checks
  }

  # ... rest of generator config ...
}

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