Initiating Conversations (P2)

Your Guide to Asking the Right Questions in consumr.ai

Picking up from where we left off in the article, “Initiating Conversations (P1),” you are now aware of the directions you need to take to reach your destination in the map of consumr.ai and begin the conversation. But the last article was purely navigational. This article will be make for an interesting read because we will learn about the types of questions we need to ask, how, and when.

So, let’s dive right in. As described in the last article, asking the right question will set you in the right direction. If you ask a question that heads you down south, where you wanted to be headed north, then you are bound to lose time, money, and put in a lot more effort. Worse, give up. So, let’s learn how to ask the right questions.

The Scope

Before we start asking questions, let us first understand the scope of conversations in consumr.ai. “In consumr.ai” being the keyword. Conversations as a term seem very chatty and broad, and you do not want to be asking dating questions to a consumer intelligence platform. So let's define our boundaries. Conversations with consumr.ai ideally work on the grounds of knowledge that the platform accumulates about the brand in discussion. This knowledge is stored and used as per the portfolio we are working with. The knowledge bank comprises 3 types of intelligence:

1. Internal: Knowledge that the platform draws from a series of reports created using consumr.ai modules.

2. Organizational: Knowledge that the user uploads onto the platform as a file. This is intelligence that is known at an organization level and is internal or even confidential to the organization. Uploading such information through a file enables consumr.ai to learn from it and help you answer a question using either the file alone, or by merging the knowledge with Internal and/or general.

3. General: This type of knowledge is usually gathered by the system if you allow the system to take creative liberties, or if you ask a question that is too broad, and the system still tries to get you an answer using the knowledge available on the internet. This happens when consumr.ai initiates a search for and gathers the top results in search and finds out the knowledge from it.

Now, going back to my casual example of asking dating questions, if your brand in the portfolio is not ‘Tinder,’ then it is likely that consumr.ai will use the third type of knowledge to still curate an answer for you, but it will not be relevant to your brand.

Precision

Now that we have our constraints in place, let us discuss the ‘Precision’ that is connected to the scope. LLMs are interesting with the way they can put things into perspective so quickly, but it also has a creative side that makes them hallucinate a bit. This is why precision is a measure that can restrict them from going to their dreamland and pinch it every now and then to keep them on track. In consumr.ai, conversations have 3 settings of precision as shown below. The user will have to select anyone of them based on the expected responses. By default, it will always be tuned to ‘Helpful’.

1. Restricted: This means that the chat will strictly be in the bounds of the added assets and use the knowledge from those assets to answer questions. If you ask it anything that is outside of consumr.ai it will politely decline or answer or plead ignorance.

2. Creative: This is the other end of the precision spectrum. This is where you allow the platform to hallucinate a bit and go outside the purview of consumr.ai and fish for answer from the oceans of the internet. This doesn’t mean that it will totally miss answering your questions, but chances are they the answers may get a little generic and may have lower reference to the data from the insights. This mode is helpful especially when you want to get context from the internet and then marry it to the data from the platform and uploaded files.

3. Helpful: When in doubt, go with neutral. Quite literally it is the best choice you can make, unless you deliberately want the model to hallucinate or strictly answer from the internal knowledge bank. The Helpful approach will get you the mix of both the worlds and give you an answer that you will be happiest with.

Asking Questions

Finally, let's jump into asking questions now that you are already aware of the pre-sets and constraints. There are two ways of asking questions:

1. Selecting one of the recommended questions.

2. Asking a custom question.

Let’s cover both in depth.

Recommended Questions:

Situation: You are on the conversations pop-up and start a new conversation. Strangely, you find the chat box to be empty and no assets are loaded. Well, it is how it is designed to be. The engine needs to know your intent and cannot guess it. So, when you initiate a new conversation, you either must ask a question and let it find the right assets to add, or you add the assets and let it recommend questions for you. In this section, let’s go with recommendations.

To get recommendation of questions to ask, click on the Add Asset button on the top of the left panel of the box.

A pop up will appear asking you to either upload an asset or select an existing asset. If you don’t have a file to upload, the guess what are you going to choose?... Don’t answer that.

Click on ‘Select Existing Assets’, to find this table of assets below. Select the assets you find are relevant and click on ‘Add Asset’ button below.

Once you do, watch consumr.ai study the assets and in seconds curate 4 recommendations of questions you can start with.

Of course, you don’t have to stick with them, but if you do, then click on any one of them and get an answer to it in seconds. Let’s select the first one.

You can see that the conversation is auto named and the engine has given you a textual reply that is simplified. However, if you want to know which asset was used to answer this question, look on the left panel while hovering on the question and the asset will be highlighted.

In case you want to see the report in detail or want to see a summary of it. Click the 3 vertical dots on the asset widget

You will get an option to open this insight in a new tab. Click it and a new tab will open with the report, just as it read in the menu.

You click on summary and the chat will summarize the report for you. Let’s click it.

In addition, you want to action an answer or add it to the bank of Research intelligence, hover on the answer to see 3 horizontal dots appear in the row of the answer. Click on the first option and the answer will be save to the knowledge bank of research, to be pulled up at another date in a conversation or other flows that permits research-based knowledge to be inherited.

If you are clueless about asking more questions, select the second option. It will give you a list of follow up questions to ask, as given below.

What questions should I ask?

Asking questions is not the difficult task. Asking the right one is. However, since this is about your business, you are the best person to do so. I have put down a list of Do’s and Don’ts for you to follow, and I'm sure it will all work out just fine. You may even find a new buddy in the form of consumr.ai.

Dos’

• Be Precise: Broader questions will get you very broad answers, and that may not help you.

• Start your questions with what.

• Keep your questions related to the assets added.

• Before you ask questions, read about the modules of consumr.ai: This will help you with the scope of your curiosity.

• Stay in the marketing arena: consumr.ai is a consumer intelligence platform, and it may not give you the right advice on other matters.

• Ask a series of questions that require shorter and specific answers. Then put them all together.

Don’ts

• Avoid starting with why: Why will bring in speculation, and the model will not take you down the right path.

• Don’t ask for opinions on strategy: There is a reason why humans are still managing companies and not AI.

• Don’t add just one asset or too many assets: Too many assets may include too much information that can confuse the model, due to contradictory information. For example, adding 20 behavior reports created with the same input, over a period of time, and then asking it to give one answer at a point in time and not asking about trends.