AI prompts for LinkedIn outreach work when they force the model to build each message from one real, verifiable detail about the prospect and one specific reason for contacting them, and fail when they ask for “a personalized message” with nothing to personalize from. The practical setup is a short chain: a research step that extracts facts from material you paste in, a connection-note step, a first-message step after they accept, and a follow-up step that adds something new instead of “bumping” the thread.
This guide gives you each of those prompts in copy-ready form, explains why every part of them is there, and walks through one illustrative prospect from research to second follow-up so you can see what the output should look like at each stage.
Why most AI-written LinkedIn messages get ignored
Most AI-written outreach is ignored because it is interchangeable: the same message could be sent to five hundred people with only the first name swapped. Recipients see a compliment about “your impressive background”, a pivot to a product, and a request for 15 minutes. None of it proves the sender looked at them.
The model is not the root problem. A language model given a vague request fills the gaps with the most statistically common version of that request, and the most common LinkedIn message is exactly the generic pitch people have learned to skip. Anthropic’s prompting guidance makes the same point in general terms: the model responds to explicit, specific instructions and to being told why a constraint exists. If you want a specific message, you have to supply specific inputs and say what “specific” means.
There are three common failure patterns worth naming before writing any prompt:
- Invented personalization. The model “references” a podcast the prospect never did or a promotion that did not happen, because you asked it to personalize without giving it source material.
- Pitch in the connection note. The note asks for a meeting before there is any relationship, which reads as a sales sequence rather than a person.
- Empty follow-ups. “Just bubbling this up” adds no reason to reply. Each follow-up should carry one new piece of value or one new question.
The prompts below are designed so each of those failures is blocked by an explicit instruction.
Step 1: Extract facts before writing anything
The research prompt turns raw profile material into a short list of facts the writing prompts are allowed to use. Separating research from writing is a simple form of prompt chaining: the model does one job at a time, and you can check the facts before any message is drafted.
Paste in what you can see yourself: the prospect’s headline, the About section, a recent post or comment, the company’s latest announcement. Do not ask the model to “look them up” from memory; a model’s training data is out of date and it may confidently produce details that are wrong.
You are helping me prepare a LinkedIn connection request. Work only from
the material inside <source> tags. Do not add facts from memory.
<source>
[Paste the prospect's headline, About section, 1-2 recent posts or
comments, and any company news you read yourself.]
</source>
<my_context>
What I do: [one sentence]
Who I help: [role + company type]
Why this person might care: [your hypothesis, 1 sentence]
</my_context>
Return:
1. FACTS: up to 5 facts about this person or their company, each with
the exact phrase from the source that supports it.
2. HOOKS: the 2 facts most likely to be a genuine reason to connect,
and one sentence on why each matters to them (not to me).
3. GAPS: anything I would need to know before messaging that the
source does not tell me.
If the source contains nothing specific enough to use, say so plainly
instead of inventing a hook.Why each part is there
- “Work only from the material inside source tags.” Restricting the model to supplied text and asking for the supporting phrase are two of the techniques Anthropic lists for reducing hallucinations. In outreach, a single invented detail destroys credibility.
- Tags around inputs. Delimiters stop the model confusing your instructions with the prospect’s text. They also make it obvious, when you reread the prompt, which text is yours and which is the prospect’s.
- “Why it matters to them (not to me).” This forces the hook to be about the recipient’s situation, which is the whole difference between a relevant message and a pitch.
- The GAPS section and the escape hatch. Giving the model permission to say “nothing usable here” is better than receiving a confident, fabricated hook. If the answer is “nothing”, the right move is often not to message that person yet.
Step 2: The connection note
A connection note is the optional short message attached to a LinkedIn invitation, and its only job is to make accepting feel reasonable. LinkedIn caps the note at a small number of characters and the limit has differed by account type over time, so check the counter in the invitation dialog rather than trusting a number from a blog post (including this one, which deliberately does not state one). Write for the strictest limit you are likely to hit.
Using only the FACTS and HOOKS above, write 3 LinkedIn connection notes.
Rules:
- Maximum [X] characters each, including spaces. Show the character
count after each note.
- Open with the specific hook, not with a compliment about the person.
- Give one honest reason I want to connect.
- No product name, no pitch, no request for a call or meeting.
- No questions that require effort to answer.
- Plain words. Avoid: "I came across your profile", "impressive",
"synergy", "I'd love to pick your brain", "hope this finds you well".
- Each version should use a different hook or a different angle on it.
Why these rules: the note is read in a list of pending invitations,
and anything that looks like a sales sequence gets ignored.Two details matter here. Asking the model to print the character count makes it check its own length, though you should still verify in the dialog because models count characters unreliably. And the closing “why these rules” line follows Anthropic’s advice to explain the motivation behind a constraint, which tends to produce output that respects the spirit of the rule rather than just its letter.
Step 3: The first message after they accept
The first message after acceptance is where you can say what you do, but it should still lead with their situation and end with a question that is easy to answer. A good question is one a busy person can reply to in a sentence from their phone.
[Name] accepted my connection request. Write the first message.
Context:
- Connection note I sent: "[paste]"
- FACTS and HOOKS: [paste from step 1]
- What I offer, in one sentence: [...]
- One concrete, relevant thing I can share for free: [a short checklist,
a data point from a public source, a relevant article, an intro]
Structure:
1. One sentence that picks up the reason I gave for connecting.
2. One or two sentences linking their situation to a problem people in
their role commonly deal with. Present it as a question or
observation, not as a diagnosis of their company.
3. Offer the free thing, no strings.
4. End with one low-effort question (yes/no or one-line answer).
Constraints: 60-90 words. No calendar link. No "quick call".
Write 2 versions: one more direct, one more conversational.The “not as a diagnosis of their company” constraint exists because an outsider telling someone what is wrong with their business is presumptuous and usually inaccurate. Framing the problem as common to the role keeps the message honest. If you need help with the question itself, the prompts for discovery call questions are a useful source of low-pressure openers.
Step 4: Follow-ups that add something new
A follow-up deserves to exist only if it gives the recipient a new reason to reply. Plan the follow-ups when you plan the first message, so each one has its own content instead of being a reworded reminder.
Plan 2 follow-ups for this LinkedIn conversation. They have not replied
to my first message: "[paste]".
For each follow-up:
- Bring exactly one new element: a different angle on their problem,
a relevant public resource, a short example of how someone in a
similar role handled it, or a narrower question.
- Do not repeat the first message's offer or wording.
- Do not guilt-trip ("just checking if you saw this").
- Under 60 words.
Follow-up 2 must be a polite close: say I won't keep messaging, and
leave the door open in one sentence.
Also tell me which follow-up you would skip if I can only send one,
and why.The polite close matters as much as the rest. It respects the person’s attention, and on LinkedIn your messages sit in a thread under your real name and photo; a sequence of increasingly pushy nudges stays visible to that person indefinitely. The same thinking applies to email, covered in more depth in the guide to sales follow-up emails.
Worked example: one prospect, start to finish
The example below is entirely illustrative: the person, company and posts are invented to show the shape of the output, not taken from a real profile.
The sender: an independent cloud cost consultant who helps mid-size SaaS companies reduce their AWS bill. The prospect: “Priya”, Head of Platform at a fictional 200-person SaaS company. Her recent post says the team just finished migrating from self-managed Kubernetes to a managed service and asks what others track after a migration like that.
Step 1 output (abridged)
- FACT: Team completed a migration to managed Kubernetes. Source phrase: “we finally finished moving off self-managed clusters”.
- FACT: She is asking peers what to monitor after migration. Source phrase: “what do you track in the first 90 days after?”
- HOOK: Her open question about post-migration tracking. Matters to her because she asked it publicly and is collecting answers.
- GAPS: No information about their cloud spend or whether cost is a concern at all.
Before and after: the connection note
A typical note from a one-line prompt (“write a personalized LinkedIn connection request to a Head of Platform”):
Hi Priya, I came across your impressive profile and would love to
connect! I help SaaS companies optimize cloud costs and would love to
share how we could help your team. Open to a quick call next week?A note from the step 2 prompt, using the extracted hook:
Hi Priya, saw your question on what to track in the 90 days after
leaving self-managed clusters. I work on that exact phase with SaaS
platform teams and would like to follow how your rollout goes.The second note proves the sender read her post, gives an honest reason to connect, and asks for nothing. It also does not claim to know anything about her costs, because the GAPS section said the source contained nothing on that.
First message and follow-ups
Thanks for connecting, Priya. On your post-migration question: one
thing platform teams often find is that per-node costs drop but
data-transfer and logging costs move in ways nobody planned for.
I keep a one-page checklist of the line items worth watching in the
first 90 days and am happy to send it over, no strings.
Is cost on your list for that period, or mostly reliability?Follow-up 1 would bring a new element, for example a link to the cloud provider’s own documentation on the cost reports most relevant after a migration. Follow-up 2 would close politely. Note that the “often find” sentence in the first message is a general observation the sender should only make if it reflects their real experience; if it does not, rewrite it around something they can stand behind.
Making AI-assisted messages sound like you
The fastest way to make outreach sound human is to give the model examples of messages you actually wrote and that people replied to. Few-shot examples are one of the most reliable ways to steer tone and format according to both OpenAI’s prompt engineering guide and Anthropic’s documentation, and they work better than adjectives like “friendly but professional”.
Here are 3 LinkedIn messages I wrote myself that got replies.
Match their length, sentence rhythm and level of formality.
Do not copy their content.
<examples>
<example>[message 1]</example>
<example>[message 2]</example>
<example>[message 3]</example>
</examples>Then run a final editing pass yourself. A short checklist that catches most problems:
- Could this message be sent to anyone else with only the name changed? If yes, it is not personalized.
- Is every fact about the person true and checked against what you read yourself?
- Would you say each sentence out loud to this person at a conference?
- Is there exactly one ask, and is it small?
- Did any stock phrase survive? The article on why ChatGPT gives generic responses explains where those phrases come from and how to prompt them out.
Where to draw the line on automation
Use AI to research and draft; send the messages yourself. LinkedIn sets its own rules on automation in its User Agreement and in a help center page on prohibited software and extensions, and those rules change. Read both before connecting any third-party tool that sends invitations or messages on your behalf, because the account at risk is yours.
There is also a quality argument. The research-then-write chain above takes a few minutes per prospect, which naturally limits volume to people you have a real reason to contact. That is the point: a small number of messages that each pass the “could this go to anyone else?” test will usually do more for you than a large volume of messages that do not.
Finally, be careful with what you paste into an AI tool. Profile text that someone published on LinkedIn is one thing; private messages, phone numbers or information from your CRM about other clients are another. Check your organization’s policy on which AI tools may receive customer and prospect data.
Questions people ask about AI and LinkedIn outreach
Should I send a connection request with no note?
For people who know you or have seen you in a shared thread, a blank request is fine. For cold prospects, a note that gives a specific reason makes acceptance more likely to be a genuine choice and gives you something to reference in the first message.
How many follow-ups is reasonable?
There is no official number. A sensible pattern is a first message plus up to two follow-ups spaced several days apart, with the last one closing the loop. If none of them got a response, the lack of reply is the answer for now.
Can I reuse these prompts for InMail or email?
Yes, with changes. Steps 1 and 4 transfer directly. For email you also need a subject line and legal requirements such as an opt-out mechanism; see the prompts for cold email for that version of the workflow.
